无线传感器网络中数据聚类方法的研究
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摘要
随着无线通讯技术、微电子技术及嵌入式计算技术的快速发展,无线传感器网络在军事国防,环境监测、交通运输等众多领域中得到广泛开的应用。如何高效的处理无线传感器网络中海量数据,以及如何从中获取有用的知识,成为新的挑战,数据挖掘中的聚类分析是解决这个问题的方法之一。然而,由于传感器节点的资源有限以及传感器节点数据具有时间和空间相关性等特点,传统的数据聚类方法很难直接应用到无线传感器网络中。
本文针对无线传感器网络中节点数据的特点,提出了一些新的方法和思路,并将该理论方法应用于无线传感器网络中。主要内容包括以下几个方面：
1.针对传感器节点资源有限及节点数据具有位置信息和感知数据的特点,提出了基于网格的分布式双重聚类算法。该算法由两级聚类构成：局部聚类和全局聚类。根据传感器节点的位置和感知数据将数据空间划分成超矩形网格单元；对相邻的网格单元合并构成连通区域,即局部的簇；从局部的簇中抽象出数据特征,将这些数据特征传送到汇聚节点上,进行全局的聚类。该算法通过减少传感器节点单跳通信距离和传送的数据量来降低网络的能量消耗。实验结果表明该算法对无线传感器网络中节点数据具有较好的聚类效果,对数据集的大小具有良好的可伸缩性,能处理大规模的数据集和发现任意形状的簇。
2.针对无线传感器节点数据具有位置信息和感知数据的特点,提出了基于模糊C均值的双重聚类算法。该算法在传统模糊C均值聚类算法的基础上插入传感器节点的位置信息,并对隶属度函数进行修正,提高了算法的性能；由于无线传感器网络的动态性,事先很难确定类的数目,采用减法聚类确定类的数目和初始类中心,从而加快了算法的收敛速度以及避免了陷入局部最优。针对无线传感器网络中节点资源有限性,采用分布式聚类,减小了传感器节点的单跳通信距离和数据的传送量,降低了网络中能量消耗。实验结果表明：相对于传统的聚类算法,该算法具有较好的聚类效果并减少了网络中能量的消耗。
3.针对传感器网络中相邻节点数据之间存在较强的相关性,提出了基于空间约束的模糊C均值聚类算法。该算法借鉴图像分割的思想,在传统的模糊C均值算法的基础上增加了一个模糊因子,该模糊因子插入了相邻传感器节点的位置信息和感知数据,使聚类结果满足簇内传感器节点在位置上是相近的,感知数据是相似的。该算法克服了模糊C均值聚类算法的不足,提高了算法的性能。实验结果表明该算法对传感器网络中节点数据具有较好的聚类效果。
4.针对基于空间约束的模糊C均值聚类算法对类边界处重叠对象分辨率不高,提出基于空间约束的粗糙模糊C均值聚类算法。该算法通过粗糙集上、下近似的引入改变了基于空间约束的模糊C均值算法中隶属度函数的分布情况,修正了类心的更新公式和模糊隶属度计算公式。该算法克服了基于空间约束的模糊C均值算法和粗糙C均值算法存在的不足,降低了计算复杂度,增强了类边界处重叠对象的分辨率。实验结果表明该算法相对于基于空间约束的模糊C均值聚类算法,性能有很好的改善。
5.高斯混合模型由于其表达灵活,已成为当前最流行的密度估计与聚类工具之一。由于传感器网络的动态性,事先很难确定高斯混合模型的成分个数；另外,在基于高斯混合模型的数据聚类过程中没有考虑传感器节点的位置信息。针对上述两个问题提出了基于空间信息的高斯混合模型,该模型将传感器节点的位置信息作为模型成分个数的先验知识。在运用期望最大化(EM)算法对该模型进行参数估计过程中,利用先验知识自动确定混合模型的成分个数。实验结果说明：相对于普通高斯混合模型,基于该混合模的EM算法能够精确的确定成分个数,对传感器网络中节点数据具有良好的聚类效果。
With the rapid development of wireless communication techniques, embedded computing techniques and microelectronics, Wireless Sensor Networks (WSNs) are being widely used in many fields, such as military defense, environment monitoring and transport. How to efficiently deal with huge amounts of sensor data in wireless sensor networks, as well as how to acquire useful knowledge, becomes a new challenge. Clustering analysis in data mining is one of the methods to solve these problems. However, it is difficult to be used directly for traditional data clustering methods in sensor networks due to limited resources on sensor node and sensor data with temporal and spatial correlation. In this thesis, we put forward some new methods and ideas for the characteristics of the sensor data in the wireless sensor network, with the main contents outlined as follows:
1. An efficient distributed dual clustering algorithm based on grid is proposed for such characteristics as the limited resources and dual attributes (location informations and sensor data) on sensor node. The proposed algorithm consists of two levels of clustering:local clustering and global clustering. First, data space is divided into hyper-rectangle grid cells according to the locations of sensor nodes and sensor data. Second, adjacent grid cells are merged by sensor nodes being location connected and similar in the same, and the features of local clustering are extracted. Then, these local features are sent to sink where global clustering is obtained based on those features. The proposed algorithm reduces the energy consumption of the network by reducing single-hop communication distance and passing data structures. The experimental results show that the proposed algorithm has a better clustering effect for sensor data, has a good scalability for the size of the data set, and can deal with large-scale data set, and find clusters with arbitrary shapes.
2. An efficient dual clustering algorithm based on fuzzy c-means is proposed for dual attributes (location informations and sensor data) on sensor data. The proposed algorithm increases positions information of the sensor nodes into the conventional fuzzy c-means algorithm, modifying membership function of the fuzzy c-means algorithm, and improves the performance of the algorithm. Subtractive clustering algorithm is used to determine the number of classes and the initial clustering center due to being difficult to determine in advance the number of classes, thus speeding up the convergence process of clustering algorithm and to avoid falling into local optimal solution. The distributed clustering is used for resource limits on sensor node, which reducing single-hop communication distance of sensor nodes and passing data structures, thereby reducing network energy consumption. Experimental results show that the algorithm has better clustering effect for sensor data and reduces network energy consumption.
3. An efficient fuzzy c-means clustering algorithm based on spatial constraints is proposed for sensor data between adjacent nodes being a strong correlation. The algorithm refers to the idea of image segmentation and incorporates the spatial information of adjacent nodes and sensor data into the conventional fuzzy c-means algorithm in a novel fuzzy way. The clustering results are the process to partition the input sensor data set into several groups in such a way that each group forms a compact region in the geographic domain while being similar in the non-geographic domain. The proposed algorithm can overcome the disadvantages of the known fuzzy c-means algorithms and at the same time enhances the clustering performance of the algorithm. The experimental results show that the algorithm has better clustering effect for sensor data.
4. A new rough fuzzy c-means clustering algorithm based on spatial constraints is proposed for being not very good for the fuzzy c-means algorithm to handle overlap of clusters and uncertainty involved in class boundary. The algorithm alters the distribution of fuzzy membership function by combining the lower approximation and upper approximation. Accordingly, the computation of clustering centroid and fuzzy membership is modified. The proposed algorithm can overcome the disadvantages of the known fuzzy c-means algorithms and rough c-means algorithms, reducing the computational complexity, increasing the resolution of boundary overlap. Experimental results show that the performance has a very good improvement with respect to the fuzzy c-means clustering algorithm based on spatial constraints.
5. Gaussian mixture model is very popular in density estimation and clustering for its expression and flexible. However, the application of Gaussian mixture model to sensor data clustering faces some difficulties. First, the estimation of the number of components is still an open question. Second, mixture-based data clustering does not consider spatial information of the sensor node, which is important for smooth regions to be obtained in the sensor data clustering results. Gaussian mixture model based on spatial information is proposed. The spatial information is used as a prior knowledge of the number of components. An expectation maximization (EM) based algorithm is developed to estimate these parameters of the proposed model using the prior knowledge of the number of components, and automatically determines the number of components. Experimental results show that the EM-based algorithm that estimates these parameters of the proposed model is capable of estimating the number of components accurately and has better clustering effect for sensor data.
本文针对无线传感器网络中节点数据的特点,提出了一些新的方法和思路,并将该理论方法应用于无线传感器网络中。主要内容包括以下几个方面：
1.针对传感器节点资源有限及节点数据具有位置信息和感知数据的特点,提出了基于网格的分布式双重聚类算法。该算法由两级聚类构成：局部聚类和全局聚类。根据传感器节点的位置和感知数据将数据空间划分成超矩形网格单元；对相邻的网格单元合并构成连通区域,即局部的簇；从局部的簇中抽象出数据特征,将这些数据特征传送到汇聚节点上,进行全局的聚类。该算法通过减少传感器节点单跳通信距离和传送的数据量来降低网络的能量消耗。实验结果表明该算法对无线传感器网络中节点数据具有较好的聚类效果,对数据集的大小具有良好的可伸缩性,能处理大规模的数据集和发现任意形状的簇。
2.针对无线传感器节点数据具有位置信息和感知数据的特点,提出了基于模糊C均值的双重聚类算法。该算法在传统模糊C均值聚类算法的基础上插入传感器节点的位置信息,并对隶属度函数进行修正,提高了算法的性能；由于无线传感器网络的动态性,事先很难确定类的数目,采用减法聚类确定类的数目和初始类中心,从而加快了算法的收敛速度以及避免了陷入局部最优。针对无线传感器网络中节点资源有限性,采用分布式聚类,减小了传感器节点的单跳通信距离和数据的传送量,降低了网络中能量消耗。实验结果表明：相对于传统的聚类算法,该算法具有较好的聚类效果并减少了网络中能量的消耗。
3.针对传感器网络中相邻节点数据之间存在较强的相关性,提出了基于空间约束的模糊C均值聚类算法。该算法借鉴图像分割的思想,在传统的模糊C均值算法的基础上增加了一个模糊因子,该模糊因子插入了相邻传感器节点的位置信息和感知数据,使聚类结果满足簇内传感器节点在位置上是相近的,感知数据是相似的。该算法克服了模糊C均值聚类算法的不足,提高了算法的性能。实验结果表明该算法对传感器网络中节点数据具有较好的聚类效果。
4.针对基于空间约束的模糊C均值聚类算法对类边界处重叠对象分辨率不高,提出基于空间约束的粗糙模糊C均值聚类算法。该算法通过粗糙集上、下近似的引入改变了基于空间约束的模糊C均值算法中隶属度函数的分布情况,修正了类心的更新公式和模糊隶属度计算公式。该算法克服了基于空间约束的模糊C均值算法和粗糙C均值算法存在的不足,降低了计算复杂度,增强了类边界处重叠对象的分辨率。实验结果表明该算法相对于基于空间约束的模糊C均值聚类算法,性能有很好的改善。
5.高斯混合模型由于其表达灵活,已成为当前最流行的密度估计与聚类工具之一。由于传感器网络的动态性,事先很难确定高斯混合模型的成分个数；另外,在基于高斯混合模型的数据聚类过程中没有考虑传感器节点的位置信息。针对上述两个问题提出了基于空间信息的高斯混合模型,该模型将传感器节点的位置信息作为模型成分个数的先验知识。在运用期望最大化(EM)算法对该模型进行参数估计过程中,利用先验知识自动确定混合模型的成分个数。实验结果说明：相对于普通高斯混合模型,基于该混合模的EM算法能够精确的确定成分个数,对传感器网络中节点数据具有良好的聚类效果。
With the rapid development of wireless communication techniques, embedded computing techniques and microelectronics, Wireless Sensor Networks (WSNs) are being widely used in many fields, such as military defense, environment monitoring and transport. How to efficiently deal with huge amounts of sensor data in wireless sensor networks, as well as how to acquire useful knowledge, becomes a new challenge. Clustering analysis in data mining is one of the methods to solve these problems. However, it is difficult to be used directly for traditional data clustering methods in sensor networks due to limited resources on sensor node and sensor data with temporal and spatial correlation. In this thesis, we put forward some new methods and ideas for the characteristics of the sensor data in the wireless sensor network, with the main contents outlined as follows:
1. An efficient distributed dual clustering algorithm based on grid is proposed for such characteristics as the limited resources and dual attributes (location informations and sensor data) on sensor node. The proposed algorithm consists of two levels of clustering:local clustering and global clustering. First, data space is divided into hyper-rectangle grid cells according to the locations of sensor nodes and sensor data. Second, adjacent grid cells are merged by sensor nodes being location connected and similar in the same, and the features of local clustering are extracted. Then, these local features are sent to sink where global clustering is obtained based on those features. The proposed algorithm reduces the energy consumption of the network by reducing single-hop communication distance and passing data structures. The experimental results show that the proposed algorithm has a better clustering effect for sensor data, has a good scalability for the size of the data set, and can deal with large-scale data set, and find clusters with arbitrary shapes.
2. An efficient dual clustering algorithm based on fuzzy c-means is proposed for dual attributes (location informations and sensor data) on sensor data. The proposed algorithm increases positions information of the sensor nodes into the conventional fuzzy c-means algorithm, modifying membership function of the fuzzy c-means algorithm, and improves the performance of the algorithm. Subtractive clustering algorithm is used to determine the number of classes and the initial clustering center due to being difficult to determine in advance the number of classes, thus speeding up the convergence process of clustering algorithm and to avoid falling into local optimal solution. The distributed clustering is used for resource limits on sensor node, which reducing single-hop communication distance of sensor nodes and passing data structures, thereby reducing network energy consumption. Experimental results show that the algorithm has better clustering effect for sensor data and reduces network energy consumption.
3. An efficient fuzzy c-means clustering algorithm based on spatial constraints is proposed for sensor data between adjacent nodes being a strong correlation. The algorithm refers to the idea of image segmentation and incorporates the spatial information of adjacent nodes and sensor data into the conventional fuzzy c-means algorithm in a novel fuzzy way. The clustering results are the process to partition the input sensor data set into several groups in such a way that each group forms a compact region in the geographic domain while being similar in the non-geographic domain. The proposed algorithm can overcome the disadvantages of the known fuzzy c-means algorithms and at the same time enhances the clustering performance of the algorithm. The experimental results show that the algorithm has better clustering effect for sensor data.
4. A new rough fuzzy c-means clustering algorithm based on spatial constraints is proposed for being not very good for the fuzzy c-means algorithm to handle overlap of clusters and uncertainty involved in class boundary. The algorithm alters the distribution of fuzzy membership function by combining the lower approximation and upper approximation. Accordingly, the computation of clustering centroid and fuzzy membership is modified. The proposed algorithm can overcome the disadvantages of the known fuzzy c-means algorithms and rough c-means algorithms, reducing the computational complexity, increasing the resolution of boundary overlap. Experimental results show that the performance has a very good improvement with respect to the fuzzy c-means clustering algorithm based on spatial constraints.
5. Gaussian mixture model is very popular in density estimation and clustering for its expression and flexible. However, the application of Gaussian mixture model to sensor data clustering faces some difficulties. First, the estimation of the number of components is still an open question. Second, mixture-based data clustering does not consider spatial information of the sensor node, which is important for smooth regions to be obtained in the sensor data clustering results. Gaussian mixture model based on spatial information is proposed. The spatial information is used as a prior knowledge of the number of components. An expectation maximization (EM) based algorithm is developed to estimate these parameters of the proposed model using the prior knowledge of the number of components, and automatically determines the number of components. Experimental results show that the EM-based algorithm that estimates these parameters of the proposed model is capable of estimating the number of components accurately and has better clustering effect for sensor data.
引文
[1]Akyildiz IF, Su W, Sankarasubramaniam Y et al. Wireless sensor networks:a survey. Computer Networks.2002,38(4), pp:393-422.
[2]Jafari R, Encarnacao A, Zahoory A, et al. Wireless sensor networks for health monitoring. In:Mobile and Ubiquitous Systems:Networking and Services, MobiQuitous 2005, The Second Annual International Conference on.2005, 6(8), pp:479-481.
[3]Szewczyk R, Osterweil E, Polastre J et al. Habitat monitoring with sensor networks. Communications of the ACM.2004,47(6), pp:34-40.
[4]崔莉,鞠海玲,苗勇.无线传感器网络研究进展.计算机研究与发展.2005,42(1),pp：163-174.
[5]MIT technology review.http://www.Technologyreview.com/.
[6]Business week online, Tech wave:The sensor revolution.http://www.business week corn/magazine/.
[7]Terry van der Werff 10 Emerging Technologies That Will Change the World, http://www. Globalfuture.com/.
[8]于海斌,曾鹏,梁韡.智能无线传感器网络系统：科学出版社,2006.
[9]Jain AK, Dubes RC. Algorithms for clustering data:Prentice-Hall, Inc,1988.
[10]Rajagopalan R, Varshney PK. Data-aggregation techniques in sensor networks: a survey. Communications Surveys & Tutorials, IEEE.2006,8(4), pp:48-63.
[11]陈宇.数据挖掘中聚类算法的研究[D].中国科学院数学与系统研维普资讯http://www. cqvip. com.2001,6.
[12]Jain AK, Murty MN, Flynn PJ. Data clustering:a review. ACM computing surveys (CSUR).1999,31(3), pp:264-323.
[13]彭柳青.高维高噪声数据聚类中关键问题研究.西安电子科技大学.2011.
[14]Leonard K, Peter J. Finding groups in data:an introduction to cluster analysis. In.:Wiley-Interscience Publication Hoboken,1990.
[15]Huang Z. A Fast Clustering Algorithm to Cluster Very Large Categorical Data Sets in Data Mining. In:DMKD:1997:Citeseer.1997.
[16]Huang Z. Clustering large data sets with mixed numeric and categorical values. In:Proceedings of the 1st Pacific-Asia Conference on Knowledge Discovery and Data Mining,(PAKDD):1997:Singapore.1997, pp:21-34.
[17]Bradley PS, Fayyad UM. Refining Initial Points for K-Means Clustering. In: ICML:1998.1998, pp:91-99.
[18]Dempster AP, Laird NM, Rubin DB. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society Series B (Methodological).1977, pp:1-38.
[19]RT N. HAN Jia wei. efficient and effective clustering methods for spatial data mining. In:Proc of the 20th International Conference on Very Large Data Bases San Francisco, CA:Morgan Kaufmann Pub lishers:1994.1994.
[20]Kaufman L, Rousseeuw PJ. Finding groups in data:an introduction to cluster analysis, vol.39:Wiley Online Library,1990.
[21]Zhang T, Ramakrishnan R, Livny M. BIRCH:an efficient data clustering method for very large databases. In:1996:ACM.1996, pp:103-114.
[22]Guha S, Rastogi R, Shim K. CURE:an efficient clustering algorithm for large databases. In:1998:ACM.1998, pp:73-84.
[23]Guha S, Rastogi R, Shim K. ROCK:A robust clustering algorithm for categorical attributes. In:1999:IEEE.1999, pp:512-521.
[24]Karypis G, Han EH, Kumar V. Chameleon:Hierarchical clustering using dynamic modeling. Computer.1999,32(8), pp:68-75.
[25]Ester M, Kriegel HP, Sander J et al. A density-based algorithm for discovering clusters in large spatial databases with noise. In:1996:AAAI Press.1996, pp:226-231.
[26]Sander J, Ester M, Kriegel HP, et al. Density-based clustering in spatial databases:The algorithm gdbscan and its applications. Data Mining and Knowledge Discovery.1998,2(2), pp:169-194.
[27]Ankerst M, Breunig MM, Kriegel HP, et al. OPTICS:ordering points to identify the clustering structure. ACM SIGMOD Record.1999,28(2), pp:49-60.
[28]Hinneburg A, Keim DA. An efficient approach to clustering in large multimedia databases with noise. In:KDD:1998.1998, pp:58-65.
[29]Agrawal R, Gehrke J, Gunopulos D, et al. Automatic subspace clustering of high dimensional data for data mining applications. Newsletter. ACM SIGMOD. 1998,2(27), pp:94-105.
[30]孙吉贵,刘杰,赵连宇.聚类算法研究.软件学报.2008,19(1),pp：48-61.
[31]Wang W, Yang J, Muntz R. STING:A statistical information grid approach to spatial data mining. In:1997:INSTITUTE OF ELECTRICAL & ELECTRONICS ENGINEERS (IEEE).1997, pp:186-195.
[32]Wang W, Yang J, Muntz R. STING+:An approach to active spatial data mining. In:1999:IEEE.1999, pp:116-125.
[33]Sheikholeslami G, Chatterjee S, Zhang A. Wavecluster:A multi-resolution clustering approach for very large spatial databases. In:1998:INSTITUTE OF ELECTRICAL & ELECTRONICS ENGINEERS.1998. pp:428-439.
[34]Kohonen T. Self-organized formation of topologically correct feature map. Biological Cybernetics.1990,2(43), pp:745-756.
[35]Carpenter GA, Grossberg S. ART 3:Hierarchical search using chemical transmitters in self-organizing pattern recognition architectures. Neural Networks.1990,3(2), pp:129-152.
[36]Law MHC, Figueiredo MAT, Jain AK. Simultaneous feature selection and clustering using mixture models. Pattern Analysis and Machine Intelligence, IEEE Transactions on.2004,26(9), pp:1154-1166.
[37]Li Y, Dong M, Hua J. Simultaneous localized feature selection and model detection for gaussian mixtures. Pattern Analysis and Machine Intelligence, IEEE Transactions on.2009,31(5), pp:953-960.
[38]Bezdek JC. Cluster validity with fuzzy sets. Cybernetics and Systems. 1973,3(3), pp:58-73.
[39]Wu KP, Wang SD. Choosing the kernel parameters for support vector machines by the inter-cluster distance in the feature space. Pattern Recognition.2009, 42(5),pp:710-717.
[40]Liu R, Wang Y, Baba T et al. SVM-based active feedback in image retrieval using clustering and unlabeled data. Pattern Recognition.2008,41(8), pp:2645-2655.
[41]Winters-Hilt S, Merat S. SVM clustering. BMC Bioinformatics.2007, 8(7).pp:18-29.
[42]Ma J, Nguyen MN, Rajapakse JC. Gene classification using codon usage and support vector machines. Computational Biology and Bioinformatics, IEEE/ACM Transactions on.2009,6(1), pp:134-143.
[43]Bicego M, Figueiredo MAT. Soft clustering using weighted one-class support vector machines. Pattern Recognition.2009,42(1), pp:27-32.
[44]Camastra F, Verri A. A novel kernel method for clustering. Pattern Analysis and Machine Intelligence. IEEE Transactions on.2005,27(5), pp:801-805.
[45]Filippone M, Camastra F, Masulli F et al. A survey of kernel and spectral methods for clustering. Pattern Recognition.2008,41(1), pp:176-190.
[46]Tushir M, Srivastava S. A new Kernelized hybrid c-mean clustering model with optimized parameters. Applied Soft Computing.2010,10(2), pp:381-389.
[47]Liao L, Lin T, Li B. MRI brain image segmentation and bias field correction based on fast spatially constrained kernel clustering approach. Pattern Recognition Letters.2008,29(10), pp:1580-1588.
[48]Von Luxburg U. A tutorial on spectral clustering. Statistics and Computing. 2007,17(4),pp:395-416.
[49]Von Luxburg U, Bousquet O, Belkin M. Limits of spectral clustering. Advances in Neural Information Processing Systems (NIPS).2005,8(17), pp:857-864.
[50]Perona P, Zelnik-Manor L. Self-tuning spectral clustering. Advances in Neural Information Processing Systems.2004,9(17), pp:1601-1608.
[51]Ng AY, Jordan MI, Weiss Y. On spectral clustering:Analysis and an algorithm. Advances in Neural Information Processing Systems.2002,6(2), pp:849-856.
[52]Ahmed A, Xing EP. Recovering time-varying networks of dependencies in social and biological studies. Proceedings of the National Academy of Sciences. 2009,106(29), pp:11878-11883.
[53]Girvan M, Newman MEJ. Community structure in social and biological networks. Proceedings of the National Academy of Sciences.2002,99(12), pp:7821.
[54]Ostling A, Harte J, Green J. Self-similarity and clustering in the spatial distribution of species. Science.2000,290(5492), pp:671-689.
[55]Maslov S, Sneppen K. Specificity and stability in topology of protein networks. Science's STKE.2002,296(5569), pp:895-910.
[56]Veenman CJ, Reinders MJT, Backer E. A maximum variance cluster algorithm. Pattern Analysis and Machine Intelligence, IEEE Transactions on.2002,24(9), pp:1273-1280.
[57]Wang JH, Rau JD, Liu WJ. Two-stage clustering via neural networks. Neural Networks, IEEE Transactions on.2003,14(3), pp:606-615.
[58]Everitt B, Landau S, Leese M. Cluster analysis. Arnold, London 2001.
[59]Cressie N. Statistics for spatial data. Terra Nova.1992,4(5), pp:613-617.
[60]Lin C-R, Liu K-H, Chen M-S. Dual Clustering:Integrating Data Clustering over Optimization and Constraint Domains. IEEE Trans on Knowl and Data Eng.2005,17(5), pp:628-637.
[61]Zhou J, Guan J, Li P. DC AD:a dual clustering algorithm for distributed spatial databases. Geo-Spatial Information Science.2007,10(2), pp:137-144.
[62]Li X, Zheng X, Yan H. On spatial clustering of combination of coordinate and attribute. Geography and Geo-Information Science.2004,20(2), pp:38-40.
[63]李光强,邓敏,程涛.一种基于双重距离的空间聚类方法.测绘学报.2009,37(4),pp：482-488.
[64]Estrin D, Govindan R, Heidemann J,et al. Next century challenges:Scalable coordination in sensor networks. In:Proceedings of the 5th annual ACM/IEEE international conference on Mobile computing and networking:1999:ACM. 1999, pp:263-270.
[65]Karp B, Kung HT. GPSR:Greedy perimeter stateless routing for wireless networks. In:Proceedings of the 6th annual international conference on Mobile computing and networking:2000:ACM.2000, pp:243-254.
[66]Li Q, De Rosa M, Rus D. Distributed algorithms for guiding navigation across a sensor network. In:Proceedings of the 9th annual international conference on Mobile computing and networking:2003:ACM.2003, pp:313-325.
[67]Meka A, Singh A. Distributed spatial clustering in sensor networks. Advances in Database Technology-EDBT.2006,5(6), pp:980-1000.
[68]Xia D, Vlajic N. Near-optimal node clustering in wireless sensor networks for environment monitoring. In:Advanced Information Networking and Applications,2007 AINA'0721st International Conference on:2007:IEEE. 2007, pp:632-641.
[69]Ma X, Li S, Luo Q et al. Distributed, hierarchical clustering and summarization in sensor networks. Advances in Data and Web Management.2007,8(9), pp:168-175.
[70]Bandyopadhyay S, Coyle EJ. An energy efficient hierarchical clustering algorithm for wireless sensor networks. In:INFOCOM 2003 Twenty-Second Annual Joint Conference of the IEEE Computer and Communications IEEE Societies:2003:IEEE.2003, pp:1713-1723.
[71]Xiang M, Luo Z, Wang P. Energy-Efficient Intra-Cluster Data Gathering of Wireless Sensor Networks. Journal of Networks.2010,5(3), pp:383.
[72]Dabirmoghaddam A, Ghaderi M, Williamson C. Energy-Efficient Clustering in Wireless Sensor Networks with Spatially Correlated Data. In:INFOCOM IEEE Conference on Computer Communications Workshops.2010,4(5), pp:15-19.
[73]Maierbacher G. Low-complexity coding and source-optimized clustering for large-scale sensor networks. ACM Trans Sen Netw.2009,5(3), pp:l-32.
[74]El Rhazi A, Pierre S. A tabu search algorithm for cluster building in wireless sensor networks. IEEE Transactions on Mobile Computing.2009,8(4), pp:433-444.
[75]Heinzelman WR, Chandrakasan A, Balakrishnan H. Energy-efficient communication protocol for wireless microsensor networks. In:System Sciences,2000 Proceedings of the 3rd Annual Hawaii International Conference on:2000:IEEE.2000,6(10), pp:34-56.
[76]Younis O, Fahmy S. Distributed clustering in ad-hoc sensor networks:A hybrid, energy-efficient approach. In:INFOCOM 2004 Twenty-third AnnualJoint Conference of the IEEE Computer and Communications Societies:2004:IEEE. 2004.
[77]Chen Q, Ma J, Zhu Y et al. An energy-efficient k-hop clustering framework for wireless sensor networks. Wireless Sensor Networks.2007,8(6), pp:17-33.
[78]Ghelichi M. RCCT:Robust clustering with cooperative transmission for energy efficient wireless sensor networks. In:Information Technology:New Generations,2008 ITNG 2008 Fifth International Conference on:2008:IEEE. 2008, pp:761-766.
[79]Li C, He L. Implementation and emulation of distributed clustering protocols for wireless sensor networks. In:Proceedings of the 2007 international conference on Wireless communications and mobile computing:2007:ACM. 2007, pp:266-271.
[80]Lin CJ, Chou PL, Chou CF. HCDD:hierarchical cluster-based data dissemination in wireless sensor networks with mobile sink. In:Proceedings of the 2006 international conference on Wireless communications and mobile computing:2006:ACM.2006,6(7), pp:1189-1194.
[81]刘刚,李志刚,周兴社.无线传感器网络聚类算法研究.计算机工程与应用.2005,2(16),,PP：18,153.
[82]沈波,张世永,钟亦平.无线传感器网络分簇路由协议.软件学报.2006,17(7),PP：1588-1600
[83]林亚平,王雷,陈宇.传感器网络中一种分布式数据汇聚层次路由算法.电子学报.2004,32(11),PP：1801-1805.
[84]Heinzelman WB. Application-specific protocol architectures for wireless networks. Massachusetts Institute of Technology.2000, pp:145-154
[85]Manjeshwar A, Agrawal DP. TEEN:a routing protocol for enhanced efficiency in wireless sensor networks.1st International Workshop on Parallel and Distributed Computing Issues in Wireless Networks and Mobile Computing. 2001, pp:2009-2015
[86]Younis O, Fahmy S. HEED:a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks. Mobile Computing, IEEE Transactions on.2004,3(4), pp:366-379.
[87]Kalpakis K, Dasgupta K, Namjoshi P. Efficient algorithms for maximum lifetime data gathering and aggregation in wireless sensor networks. Computer Networks.2003,42(6),pp:697-716.
[88]Harmer PK, Williams PD, Gunsch GH et al. An artificial immune system architecture for computer security applications. Evolutionary Computation, IEEE Transactions on.2002,6(3), pp:252-280.
[89]Chen Y, Tu L. Density-based clustering for real-time stream data. In: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining:2007:ACM.2007, pp:133-142.
[90]Han J, Kamber M. Data mining:concepts and techniques. Morgan Kaufmann, 2006.
[91]Blackman SS, Popoli R. Design and analysis of modern tracking systems, vol. 685:Artech House Norwood, MA,1999.
[92]Bohm C, Plant C. Hissclu:a hierarchical density-based method for semi-supervised clustering. In:Proceedings of the 11th international conference on Extending database technology:Advances in database technology:2008: ACM.2008, pp:440-451.
[93]Hu H, Ma X, Tang S et al. MCC:model-based continuous clustering in wireless sensor networks. In:Fuzzy Systems and Knowledge Discovery,2009 FSKD'09 Sixth International Conference on:2009:IEEE.2009, pp:265-269.
[94]Ben-Hur A, Horn D, Siegelmann HT et al. Support vector clustering.2002,3(2), pp:125-137.
[95]Hush D, Scovel C. Polynomial-Time Decomposition Algorithms for Support Vector Machines. Machine Learning.2003,51(1), pp:51-71.
[96]King B. Step-wise clustering procedures. Journal of the American Statistical Association.1967,8(4),pp:86-101.
[97]Tai C-H, Dai B-R, Chen M-S. Incremental Clustering in Geography and Optimization Spaces. In:Advances in Knowledge Discovery and Data Mining. 2007,8(9), pp:272-283.
[98]Zhang B, Yin WJ, Xie M et al. Geo-spatial clustering with non-spatial attributes and geographic non-overlapping constraint:a penalized spatial distance measure. In:Advances in Knowledge Discovery and Data Mining, edn.: Springer.2007, pp:1072-1079.
[99]Lo C-H, Peng W-C. Efficient Joint Clustering Algorithms in Optimization and Geography Domains.2008,12(9), pp:945-950.
[100]Wei L-Y, Peng W-C. Clustering Data Streams in Optimization and Geography Domains. In:Advances in Knowledge Discovery and Data Mining.2009,9(6), pp:997-1005.
[101]孙利民.无线传感器网络.清华大学出版社,2005.
[102]黄如.面向数据的无线传感器网络节能机制研究.上海交通大学.2008.
[103]郑杰.无线传感器网络周期性数据收集研究.中国科学技术大学.2010.
[104]郑国强.无线传感器网络的能量高效数据收集技术研究.西安电子科技大学.2011.
[105]周四望.无线传感器网络中的数据收集算法研究.湖南大学.2007.
[106]Ilyas M, Mahgoub I. Handbook of sensor networks:compact wireless and wired sensing systems:CRC.2005,6(16),pp:2410-2438.
[107]Akkaya K, Younis M. A survey on routing protocols for wireless sensor networks. Ad Hoc Networks.2005,3(3),pp:325-349.
[108]Potdar V, Sharif A, Chang E. Wireless sensor networks:A survey. In:2009: IEEE.2009, pp:636-641.
[109]Akyildiz IF, Su W, Sankarasubramaniam Y, et al. A CCEPTED FROM O PEN C ALL A Survey on Sensor Networks. IEEE Communications Magazine. 2002,8(3), pp:102-114.
[110]Luo J, Hubaux JP. Joint mobility and routing for lifetime elongation in wireless sensor networks. In:2005:IEEE.2005,1(1733), pp:1735-1746.
[111]Davies DL, Bouldin DW. A cluster separation measure. Pattern Analysis and Machine Intelligence, IEEE Transactions on.1979,3(2), pp:224-227.
[112]王丹,吴孟达.粗糙模糊C均值融合聚类.国防科技大学学报.2011,33(3),PP：145-150.
[113]Krinidis S, Chatzis V. A Robust Fuzzy Local Information C-Means Clustering Algorithm. Image Processing, IEEE Transactions on.2010,19(5),pp:1328-1337.
[114]Gale S, Olsson G. Philosophy in geography. NewZealand.l981,l(13),pp:40-41.
[115]Bradley P, Bennett K, Demiriz A. Constrained k-means clustering. Microsoft Research,2000.
[116]Estivill V, Lee I. Autoclust+:Automatic clustering of point-data sets in the presence of obstacles. Temporal, Spatial, and Spatio-Temporal Data Mining. 2001,6(9),pp:133-146.
[117]朱明.数据挖掘：中国科学技术大学出版社,2008.
[118]Xu Y, Heidemann J, Estrin D. Geography-informed energy conservation for ad hoc routing. In:2001:ACM.2001,pp:84-96.
[119]Alsuwaiyel M. Algorithms:design techniques and analysis:World Scientific Pub Co Inc,1999.
[120]孙玉芬.基于网格方法的聚类算法研究.华中科技大学.2006.
[121]Asuncion A, Newman DJ. UCI Machine Learning Repository [http://www. ics. uci. edu/-mlearn/MLRepository. html]. Irvine, CA:University of California. School of Information and Computer Science.2007.
[122]Xu R, Donald Wunsch I. Survey of Clustering Algorithms. IEEE Transactions on Neural Networks.2005,16(3), pp:645-668.
[123]高新波.模糊聚类分析及其应用：西安电子科技大学出版,2004.
[124]张敏,于剑.基于划分的模糊聚类算法.软件学报.2004,15(6),pp：858-868.
[125]Bezdek J. Fuzzy mathematics in pattern classification:Cornell University, 1973,8(15),pp:567-579.
[126]Bezdek J. A convergence theorem for the fuzzy ISODATA clustering algorithms.2009,1(2), pp:1-8.
[127]Bezdek J:Pattern recognition with fuzzy object algorithms. In.:New York: Plenum Press,1981.
[128]Chiu S. Fuzzy model identification based on cluster estimation. Journal of Intelligent and Fuzzy Systems.1994,2(3), pp:267-278.
[129]裴继红,范九伦,谢维信.聚类中心的初始化方法.电子科学学刊.1999,2(3),pp：320-325.
[130]Pottie GJ, Kaiser WJ. Wireless integrated network sensors. Communications of the ACM.2000,43(5), pp:51-58.
[131]Heinzelman W, Chandrakasan A, Balakrishnan H et al. An application-specific protocol architecture for wireless microsensor networks. IEEE Transactions on Wireless Communications.2002,1(4), pp:660-670.
[132]Mhatre V, Rosenberg C. Design guidelines for wireless sensor networks: communication, clustering and aggregation. Ad Hoc Networks.2004,2(1), pp:45-63.
[133]Bezdek JC. Pattern recognition with fuzzy objective function algorithms: Kluwer Academic Publishers,1981.
[134]Lingras P, West C. Interval set clustering of web users with rough k-means. Journal of Intelligent Information Systems.2004,23(1), pp:5-16.
[135]Yang MS, Wu KL. Unsupervised possibilistic clustering. Pattern Recognition. 2006,39(1), pp:5-21.
[136]Yang MS, Tsai HS. A Gaussian kernel-based fuzzy c-means algorithm with a spatial bias correction.Pattern Recognition Letters.2008,29(12), pp:1713-1725.
[137]Hou Z, Qian W, Huang S et al. Regularized fuzzy c-means method for brain tissue clustering. Pattern Recognition Letters.2007,28(13), pp:1788-1794.
[138]Karmakar GC, Dooley L. A generic fuzzy rule based technique for image segmentation. In:Acoustics, Speech, and Signal Processing,2001 Proceedings(ICASSP'01) 2001 IEEE International Conference on:2001:IEEE. 2001, pp:1577-1580.
[139]Pham DL. Fuzzy clustering with spatial constraints. In:Image Processing 2002 Proceedings 2002 International Conference on:IEEE.2002,3(62), pp:65-68.
[140]Wang X, Wang Y, Wang L. Improving fuzzy c-means clustering based on feature-weight learning. Pattern Recognition Letters.2004,25(10), pp:123-132.
[141]Wu KL, Yang MS. Alternative c-means clustering algorithms. Pattern Recognition.2002,35(10), pp:2267-2278.
[142]Maji P, Pal SK. Rough set based generalized fuzzy c-means algorithm and quantitative indices. IEEE Transactions on Systems, Man, and Cybernetics, Part B.2007,37(6), pp:1529-1540.
[143]Mitra S, Banka H, Pedrycz W. Rough-fuzzy collaborative clustering. Systems, Man, and Cybernetics, Part B:Cybernetics, IEEE Transactions on.2006,36(4), pp:795-805.
[144]Pawlak Z. Rough Sets:Theoretical Aspects of Reasoning about Data. Volume 9 of System Theory, Knowledge Engineering and Problem Solving. In.:Kluwer Academic Publishers, Dordrecht, The Netherlands,1991.
[145]Han B, Wu TJ. Data mining in multisensor system based on rough set theory. In: American Control Conference,2001 Proceedings of the:IEEE.2001, pp: 4427-4431.
[146]Lingras P, Chen M, Miao D. Precision of Rough Set Clustering. In:Rough Sets and Current Trends in Computing.2008,9(8), pp:369-378.
[147]Lingras P, Yan R, Jain A. Web usage mining:Comparison of conventional, fuzzy, and rough set clustering. Computational Web Intelligence:Intelligent Technology for Web Applications, Springer.2004, pp:133-148.
[148]Dubois D, Prade H. Rough fuzzy sets and fuzzy rough sets. International Journal of General System.1990,17(2), pp:191-209.
[149]Mitra S, Banka H, Pedrycz W. Rough-fuzzy collaborative clustering. IEEE Transactions on Systems, Man, and Cybernetics, Part B:Cybernetics.2006, 36(4), pp:795-805.
[150]Maji P, Pal SK. Rough set based generalized fuzzy C-Means algorithm and quantitative indices. IEEE Transactions on Systems, Man, and Cybernetics, Part B:Cybernetics.2007,37(6), pp:1529-1540.
[151]Intan R, Mukaidono M. Generalized fuzzy rough sets by conditional probability relations. International Journal of Pattern Recognition and Artificial Intelligence.2002,16(7), pp:865-881.
[152]Nowak RD. Distributed EM algorithms for density estimation and clustering in sensor networks. IEEE Transactions on Signal Processing.2003,51(8), pp:2245-2253.
[153]Kamimura R. Cooperative information maximization with Gaussian activation functions for self-organizing maps. Neural Networks, IEEE Transactions on. 2006,17(4),pp:909-918.
[154]Safarinejadian B, Menhaj M, Karrari M. Distributed variational Bayesian algorithms for Gaussian mixtures in sensor networks. Signal Processing.2010, 90(4), pp:1197-1208.
[155]Gu D. Distributed EM algorithm for Gaussian mixtures in sensor networks. IEEE Transactions on Neural Networks.2008,19(7), pp:235-248.
[156]Safarinejadian B, Menhaj MB, Karrari M. Distributed Unsupervised Gaussian Mixture Learning for Density Estimation in Sensor Networks. Instrumentation and Measurement, IEEE Transactions on.2010,59(9), pp:2250-2260.
[157]Figueiredo M, Jain AK. Unsupervised learning of finite mixture models. Pattern Analysis and Machine Intelligence, IEEE Transactions on.2002,24(3), pp:381-396.
[158]McLachlan GJ, Peel D. Finite mixture models. New York:Wiley-Interscience, 2004.
[159]Figueiredo MAT, Jain AK. Unsupervised learning of finite mixture models. Pattern Analysis and Machine Intelligence, IEEE Transactions on.2002,24(3), pp:381-396.
[2]Jafari R, Encarnacao A, Zahoory A, et al. Wireless sensor networks for health monitoring. In:Mobile and Ubiquitous Systems:Networking and Services, MobiQuitous 2005, The Second Annual International Conference on.2005, 6(8), pp:479-481.
[3]Szewczyk R, Osterweil E, Polastre J et al. Habitat monitoring with sensor networks. Communications of the ACM.2004,47(6), pp:34-40.
[4]崔莉,鞠海玲,苗勇.无线传感器网络研究进展.计算机研究与发展.2005,42(1),pp：163-174.
[5]MIT technology review.http://www.Technologyreview.com/.
[6]Business week online, Tech wave:The sensor revolution.http://www.business week corn/magazine/.
[7]Terry van der Werff 10 Emerging Technologies That Will Change the World, http://www. Globalfuture.com/.
[8]于海斌,曾鹏,梁韡.智能无线传感器网络系统：科学出版社,2006.
[9]Jain AK, Dubes RC. Algorithms for clustering data:Prentice-Hall, Inc,1988.
[10]Rajagopalan R, Varshney PK. Data-aggregation techniques in sensor networks: a survey. Communications Surveys & Tutorials, IEEE.2006,8(4), pp:48-63.
[11]陈宇.数据挖掘中聚类算法的研究[D].中国科学院数学与系统研维普资讯http://www. cqvip. com.2001,6.
[12]Jain AK, Murty MN, Flynn PJ. Data clustering:a review. ACM computing surveys (CSUR).1999,31(3), pp:264-323.
[13]彭柳青.高维高噪声数据聚类中关键问题研究.西安电子科技大学.2011.
[14]Leonard K, Peter J. Finding groups in data:an introduction to cluster analysis. In.:Wiley-Interscience Publication Hoboken,1990.
[15]Huang Z. A Fast Clustering Algorithm to Cluster Very Large Categorical Data Sets in Data Mining. In:DMKD:1997:Citeseer.1997.
[16]Huang Z. Clustering large data sets with mixed numeric and categorical values. In:Proceedings of the 1st Pacific-Asia Conference on Knowledge Discovery and Data Mining,(PAKDD):1997:Singapore.1997, pp:21-34.
[17]Bradley PS, Fayyad UM. Refining Initial Points for K-Means Clustering. In: ICML:1998.1998, pp:91-99.
[18]Dempster AP, Laird NM, Rubin DB. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society Series B (Methodological).1977, pp:1-38.
[19]RT N. HAN Jia wei. efficient and effective clustering methods for spatial data mining. In:Proc of the 20th International Conference on Very Large Data Bases San Francisco, CA:Morgan Kaufmann Pub lishers:1994.1994.
[20]Kaufman L, Rousseeuw PJ. Finding groups in data:an introduction to cluster analysis, vol.39:Wiley Online Library,1990.
[21]Zhang T, Ramakrishnan R, Livny M. BIRCH:an efficient data clustering method for very large databases. In:1996:ACM.1996, pp:103-114.
[22]Guha S, Rastogi R, Shim K. CURE:an efficient clustering algorithm for large databases. In:1998:ACM.1998, pp:73-84.
[23]Guha S, Rastogi R, Shim K. ROCK:A robust clustering algorithm for categorical attributes. In:1999:IEEE.1999, pp:512-521.
[24]Karypis G, Han EH, Kumar V. Chameleon:Hierarchical clustering using dynamic modeling. Computer.1999,32(8), pp:68-75.
[25]Ester M, Kriegel HP, Sander J et al. A density-based algorithm for discovering clusters in large spatial databases with noise. In:1996:AAAI Press.1996, pp:226-231.
[26]Sander J, Ester M, Kriegel HP, et al. Density-based clustering in spatial databases:The algorithm gdbscan and its applications. Data Mining and Knowledge Discovery.1998,2(2), pp:169-194.
[27]Ankerst M, Breunig MM, Kriegel HP, et al. OPTICS:ordering points to identify the clustering structure. ACM SIGMOD Record.1999,28(2), pp:49-60.
[28]Hinneburg A, Keim DA. An efficient approach to clustering in large multimedia databases with noise. In:KDD:1998.1998, pp:58-65.
[29]Agrawal R, Gehrke J, Gunopulos D, et al. Automatic subspace clustering of high dimensional data for data mining applications. Newsletter. ACM SIGMOD. 1998,2(27), pp:94-105.
[30]孙吉贵,刘杰,赵连宇.聚类算法研究.软件学报.2008,19(1),pp：48-61.
[31]Wang W, Yang J, Muntz R. STING:A statistical information grid approach to spatial data mining. In:1997:INSTITUTE OF ELECTRICAL & ELECTRONICS ENGINEERS (IEEE).1997, pp:186-195.
[32]Wang W, Yang J, Muntz R. STING+:An approach to active spatial data mining. In:1999:IEEE.1999, pp:116-125.
[33]Sheikholeslami G, Chatterjee S, Zhang A. Wavecluster:A multi-resolution clustering approach for very large spatial databases. In:1998:INSTITUTE OF ELECTRICAL & ELECTRONICS ENGINEERS.1998. pp:428-439.
[34]Kohonen T. Self-organized formation of topologically correct feature map. Biological Cybernetics.1990,2(43), pp:745-756.
[35]Carpenter GA, Grossberg S. ART 3:Hierarchical search using chemical transmitters in self-organizing pattern recognition architectures. Neural Networks.1990,3(2), pp:129-152.
[36]Law MHC, Figueiredo MAT, Jain AK. Simultaneous feature selection and clustering using mixture models. Pattern Analysis and Machine Intelligence, IEEE Transactions on.2004,26(9), pp:1154-1166.
[37]Li Y, Dong M, Hua J. Simultaneous localized feature selection and model detection for gaussian mixtures. Pattern Analysis and Machine Intelligence, IEEE Transactions on.2009,31(5), pp:953-960.
[38]Bezdek JC. Cluster validity with fuzzy sets. Cybernetics and Systems. 1973,3(3), pp:58-73.
[39]Wu KP, Wang SD. Choosing the kernel parameters for support vector machines by the inter-cluster distance in the feature space. Pattern Recognition.2009, 42(5),pp:710-717.
[40]Liu R, Wang Y, Baba T et al. SVM-based active feedback in image retrieval using clustering and unlabeled data. Pattern Recognition.2008,41(8), pp:2645-2655.
[41]Winters-Hilt S, Merat S. SVM clustering. BMC Bioinformatics.2007, 8(7).pp:18-29.
[42]Ma J, Nguyen MN, Rajapakse JC. Gene classification using codon usage and support vector machines. Computational Biology and Bioinformatics, IEEE/ACM Transactions on.2009,6(1), pp:134-143.
[43]Bicego M, Figueiredo MAT. Soft clustering using weighted one-class support vector machines. Pattern Recognition.2009,42(1), pp:27-32.
[44]Camastra F, Verri A. A novel kernel method for clustering. Pattern Analysis and Machine Intelligence. IEEE Transactions on.2005,27(5), pp:801-805.
[45]Filippone M, Camastra F, Masulli F et al. A survey of kernel and spectral methods for clustering. Pattern Recognition.2008,41(1), pp:176-190.
[46]Tushir M, Srivastava S. A new Kernelized hybrid c-mean clustering model with optimized parameters. Applied Soft Computing.2010,10(2), pp:381-389.
[47]Liao L, Lin T, Li B. MRI brain image segmentation and bias field correction based on fast spatially constrained kernel clustering approach. Pattern Recognition Letters.2008,29(10), pp:1580-1588.
[48]Von Luxburg U. A tutorial on spectral clustering. Statistics and Computing. 2007,17(4),pp:395-416.
[49]Von Luxburg U, Bousquet O, Belkin M. Limits of spectral clustering. Advances in Neural Information Processing Systems (NIPS).2005,8(17), pp:857-864.
[50]Perona P, Zelnik-Manor L. Self-tuning spectral clustering. Advances in Neural Information Processing Systems.2004,9(17), pp:1601-1608.
[51]Ng AY, Jordan MI, Weiss Y. On spectral clustering:Analysis and an algorithm. Advances in Neural Information Processing Systems.2002,6(2), pp:849-856.
[52]Ahmed A, Xing EP. Recovering time-varying networks of dependencies in social and biological studies. Proceedings of the National Academy of Sciences. 2009,106(29), pp:11878-11883.
[53]Girvan M, Newman MEJ. Community structure in social and biological networks. Proceedings of the National Academy of Sciences.2002,99(12), pp:7821.
[54]Ostling A, Harte J, Green J. Self-similarity and clustering in the spatial distribution of species. Science.2000,290(5492), pp:671-689.
[55]Maslov S, Sneppen K. Specificity and stability in topology of protein networks. Science's STKE.2002,296(5569), pp:895-910.
[56]Veenman CJ, Reinders MJT, Backer E. A maximum variance cluster algorithm. Pattern Analysis and Machine Intelligence, IEEE Transactions on.2002,24(9), pp:1273-1280.
[57]Wang JH, Rau JD, Liu WJ. Two-stage clustering via neural networks. Neural Networks, IEEE Transactions on.2003,14(3), pp:606-615.
[58]Everitt B, Landau S, Leese M. Cluster analysis. Arnold, London 2001.
[59]Cressie N. Statistics for spatial data. Terra Nova.1992,4(5), pp:613-617.
[60]Lin C-R, Liu K-H, Chen M-S. Dual Clustering:Integrating Data Clustering over Optimization and Constraint Domains. IEEE Trans on Knowl and Data Eng.2005,17(5), pp:628-637.
[61]Zhou J, Guan J, Li P. DC AD:a dual clustering algorithm for distributed spatial databases. Geo-Spatial Information Science.2007,10(2), pp:137-144.
[62]Li X, Zheng X, Yan H. On spatial clustering of combination of coordinate and attribute. Geography and Geo-Information Science.2004,20(2), pp:38-40.
[63]李光强,邓敏,程涛.一种基于双重距离的空间聚类方法.测绘学报.2009,37(4),pp：482-488.
[64]Estrin D, Govindan R, Heidemann J,et al. Next century challenges:Scalable coordination in sensor networks. In:Proceedings of the 5th annual ACM/IEEE international conference on Mobile computing and networking:1999:ACM. 1999, pp:263-270.
[65]Karp B, Kung HT. GPSR:Greedy perimeter stateless routing for wireless networks. In:Proceedings of the 6th annual international conference on Mobile computing and networking:2000:ACM.2000, pp:243-254.
[66]Li Q, De Rosa M, Rus D. Distributed algorithms for guiding navigation across a sensor network. In:Proceedings of the 9th annual international conference on Mobile computing and networking:2003:ACM.2003, pp:313-325.
[67]Meka A, Singh A. Distributed spatial clustering in sensor networks. Advances in Database Technology-EDBT.2006,5(6), pp:980-1000.
[68]Xia D, Vlajic N. Near-optimal node clustering in wireless sensor networks for environment monitoring. In:Advanced Information Networking and Applications,2007 AINA'0721st International Conference on:2007:IEEE. 2007, pp:632-641.
[69]Ma X, Li S, Luo Q et al. Distributed, hierarchical clustering and summarization in sensor networks. Advances in Data and Web Management.2007,8(9), pp:168-175.
[70]Bandyopadhyay S, Coyle EJ. An energy efficient hierarchical clustering algorithm for wireless sensor networks. In:INFOCOM 2003 Twenty-Second Annual Joint Conference of the IEEE Computer and Communications IEEE Societies:2003:IEEE.2003, pp:1713-1723.
[71]Xiang M, Luo Z, Wang P. Energy-Efficient Intra-Cluster Data Gathering of Wireless Sensor Networks. Journal of Networks.2010,5(3), pp:383.
[72]Dabirmoghaddam A, Ghaderi M, Williamson C. Energy-Efficient Clustering in Wireless Sensor Networks with Spatially Correlated Data. In:INFOCOM IEEE Conference on Computer Communications Workshops.2010,4(5), pp:15-19.
[73]Maierbacher G. Low-complexity coding and source-optimized clustering for large-scale sensor networks. ACM Trans Sen Netw.2009,5(3), pp:l-32.
[74]El Rhazi A, Pierre S. A tabu search algorithm for cluster building in wireless sensor networks. IEEE Transactions on Mobile Computing.2009,8(4), pp:433-444.
[75]Heinzelman WR, Chandrakasan A, Balakrishnan H. Energy-efficient communication protocol for wireless microsensor networks. In:System Sciences,2000 Proceedings of the 3rd Annual Hawaii International Conference on:2000:IEEE.2000,6(10), pp:34-56.
[76]Younis O, Fahmy S. Distributed clustering in ad-hoc sensor networks:A hybrid, energy-efficient approach. In:INFOCOM 2004 Twenty-third AnnualJoint Conference of the IEEE Computer and Communications Societies:2004:IEEE. 2004.
[77]Chen Q, Ma J, Zhu Y et al. An energy-efficient k-hop clustering framework for wireless sensor networks. Wireless Sensor Networks.2007,8(6), pp:17-33.
[78]Ghelichi M. RCCT:Robust clustering with cooperative transmission for energy efficient wireless sensor networks. In:Information Technology:New Generations,2008 ITNG 2008 Fifth International Conference on:2008:IEEE. 2008, pp:761-766.
[79]Li C, He L. Implementation and emulation of distributed clustering protocols for wireless sensor networks. In:Proceedings of the 2007 international conference on Wireless communications and mobile computing:2007:ACM. 2007, pp:266-271.
[80]Lin CJ, Chou PL, Chou CF. HCDD:hierarchical cluster-based data dissemination in wireless sensor networks with mobile sink. In:Proceedings of the 2006 international conference on Wireless communications and mobile computing:2006:ACM.2006,6(7), pp:1189-1194.
[81]刘刚,李志刚,周兴社.无线传感器网络聚类算法研究.计算机工程与应用.2005,2(16),,PP：18,153.
[82]沈波,张世永,钟亦平.无线传感器网络分簇路由协议.软件学报.2006,17(7),PP：1588-1600
[83]林亚平,王雷,陈宇.传感器网络中一种分布式数据汇聚层次路由算法.电子学报.2004,32(11),PP：1801-1805.
[84]Heinzelman WB. Application-specific protocol architectures for wireless networks. Massachusetts Institute of Technology.2000, pp:145-154
[85]Manjeshwar A, Agrawal DP. TEEN:a routing protocol for enhanced efficiency in wireless sensor networks.1st International Workshop on Parallel and Distributed Computing Issues in Wireless Networks and Mobile Computing. 2001, pp:2009-2015
[86]Younis O, Fahmy S. HEED:a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks. Mobile Computing, IEEE Transactions on.2004,3(4), pp:366-379.
[87]Kalpakis K, Dasgupta K, Namjoshi P. Efficient algorithms for maximum lifetime data gathering and aggregation in wireless sensor networks. Computer Networks.2003,42(6),pp:697-716.
[88]Harmer PK, Williams PD, Gunsch GH et al. An artificial immune system architecture for computer security applications. Evolutionary Computation, IEEE Transactions on.2002,6(3), pp:252-280.
[89]Chen Y, Tu L. Density-based clustering for real-time stream data. In: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining:2007:ACM.2007, pp:133-142.
[90]Han J, Kamber M. Data mining:concepts and techniques. Morgan Kaufmann, 2006.
[91]Blackman SS, Popoli R. Design and analysis of modern tracking systems, vol. 685:Artech House Norwood, MA,1999.
[92]Bohm C, Plant C. Hissclu:a hierarchical density-based method for semi-supervised clustering. In:Proceedings of the 11th international conference on Extending database technology:Advances in database technology:2008: ACM.2008, pp:440-451.
[93]Hu H, Ma X, Tang S et al. MCC:model-based continuous clustering in wireless sensor networks. In:Fuzzy Systems and Knowledge Discovery,2009 FSKD'09 Sixth International Conference on:2009:IEEE.2009, pp:265-269.
[94]Ben-Hur A, Horn D, Siegelmann HT et al. Support vector clustering.2002,3(2), pp:125-137.
[95]Hush D, Scovel C. Polynomial-Time Decomposition Algorithms for Support Vector Machines. Machine Learning.2003,51(1), pp:51-71.
[96]King B. Step-wise clustering procedures. Journal of the American Statistical Association.1967,8(4),pp:86-101.
[97]Tai C-H, Dai B-R, Chen M-S. Incremental Clustering in Geography and Optimization Spaces. In:Advances in Knowledge Discovery and Data Mining. 2007,8(9), pp:272-283.
[98]Zhang B, Yin WJ, Xie M et al. Geo-spatial clustering with non-spatial attributes and geographic non-overlapping constraint:a penalized spatial distance measure. In:Advances in Knowledge Discovery and Data Mining, edn.: Springer.2007, pp:1072-1079.
[99]Lo C-H, Peng W-C. Efficient Joint Clustering Algorithms in Optimization and Geography Domains.2008,12(9), pp:945-950.
[100]Wei L-Y, Peng W-C. Clustering Data Streams in Optimization and Geography Domains. In:Advances in Knowledge Discovery and Data Mining.2009,9(6), pp:997-1005.
[101]孙利民.无线传感器网络.清华大学出版社,2005.
[102]黄如.面向数据的无线传感器网络节能机制研究.上海交通大学.2008.
[103]郑杰.无线传感器网络周期性数据收集研究.中国科学技术大学.2010.
[104]郑国强.无线传感器网络的能量高效数据收集技术研究.西安电子科技大学.2011.
[105]周四望.无线传感器网络中的数据收集算法研究.湖南大学.2007.
[106]Ilyas M, Mahgoub I. Handbook of sensor networks:compact wireless and wired sensing systems:CRC.2005,6(16),pp:2410-2438.
[107]Akkaya K, Younis M. A survey on routing protocols for wireless sensor networks. Ad Hoc Networks.2005,3(3),pp:325-349.
[108]Potdar V, Sharif A, Chang E. Wireless sensor networks:A survey. In:2009: IEEE.2009, pp:636-641.
[109]Akyildiz IF, Su W, Sankarasubramaniam Y, et al. A CCEPTED FROM O PEN C ALL A Survey on Sensor Networks. IEEE Communications Magazine. 2002,8(3), pp:102-114.
[110]Luo J, Hubaux JP. Joint mobility and routing for lifetime elongation in wireless sensor networks. In:2005:IEEE.2005,1(1733), pp:1735-1746.
[111]Davies DL, Bouldin DW. A cluster separation measure. Pattern Analysis and Machine Intelligence, IEEE Transactions on.1979,3(2), pp:224-227.
[112]王丹,吴孟达.粗糙模糊C均值融合聚类.国防科技大学学报.2011,33(3),PP：145-150.
[113]Krinidis S, Chatzis V. A Robust Fuzzy Local Information C-Means Clustering Algorithm. Image Processing, IEEE Transactions on.2010,19(5),pp:1328-1337.
[114]Gale S, Olsson G. Philosophy in geography. NewZealand.l981,l(13),pp:40-41.
[115]Bradley P, Bennett K, Demiriz A. Constrained k-means clustering. Microsoft Research,2000.
[116]Estivill V, Lee I. Autoclust+:Automatic clustering of point-data sets in the presence of obstacles. Temporal, Spatial, and Spatio-Temporal Data Mining. 2001,6(9),pp:133-146.
[117]朱明.数据挖掘：中国科学技术大学出版社,2008.
[118]Xu Y, Heidemann J, Estrin D. Geography-informed energy conservation for ad hoc routing. In:2001:ACM.2001,pp:84-96.
[119]Alsuwaiyel M. Algorithms:design techniques and analysis:World Scientific Pub Co Inc,1999.
[120]孙玉芬.基于网格方法的聚类算法研究.华中科技大学.2006.
[121]Asuncion A, Newman DJ. UCI Machine Learning Repository [http://www. ics. uci. edu/-mlearn/MLRepository. html]. Irvine, CA:University of California. School of Information and Computer Science.2007.
[122]Xu R, Donald Wunsch I. Survey of Clustering Algorithms. IEEE Transactions on Neural Networks.2005,16(3), pp:645-668.
[123]高新波.模糊聚类分析及其应用：西安电子科技大学出版,2004.
[124]张敏,于剑.基于划分的模糊聚类算法.软件学报.2004,15(6),pp：858-868.
[125]Bezdek J. Fuzzy mathematics in pattern classification:Cornell University, 1973,8(15),pp:567-579.
[126]Bezdek J. A convergence theorem for the fuzzy ISODATA clustering algorithms.2009,1(2), pp:1-8.
[127]Bezdek J:Pattern recognition with fuzzy object algorithms. In.:New York: Plenum Press,1981.
[128]Chiu S. Fuzzy model identification based on cluster estimation. Journal of Intelligent and Fuzzy Systems.1994,2(3), pp:267-278.
[129]裴继红,范九伦,谢维信.聚类中心的初始化方法.电子科学学刊.1999,2(3),pp：320-325.
[130]Pottie GJ, Kaiser WJ. Wireless integrated network sensors. Communications of the ACM.2000,43(5), pp:51-58.
[131]Heinzelman W, Chandrakasan A, Balakrishnan H et al. An application-specific protocol architecture for wireless microsensor networks. IEEE Transactions on Wireless Communications.2002,1(4), pp:660-670.
[132]Mhatre V, Rosenberg C. Design guidelines for wireless sensor networks: communication, clustering and aggregation. Ad Hoc Networks.2004,2(1), pp:45-63.
[133]Bezdek JC. Pattern recognition with fuzzy objective function algorithms: Kluwer Academic Publishers,1981.
[134]Lingras P, West C. Interval set clustering of web users with rough k-means. Journal of Intelligent Information Systems.2004,23(1), pp:5-16.
[135]Yang MS, Wu KL. Unsupervised possibilistic clustering. Pattern Recognition. 2006,39(1), pp:5-21.
[136]Yang MS, Tsai HS. A Gaussian kernel-based fuzzy c-means algorithm with a spatial bias correction.Pattern Recognition Letters.2008,29(12), pp:1713-1725.
[137]Hou Z, Qian W, Huang S et al. Regularized fuzzy c-means method for brain tissue clustering. Pattern Recognition Letters.2007,28(13), pp:1788-1794.
[138]Karmakar GC, Dooley L. A generic fuzzy rule based technique for image segmentation. In:Acoustics, Speech, and Signal Processing,2001 Proceedings(ICASSP'01) 2001 IEEE International Conference on:2001:IEEE. 2001, pp:1577-1580.
[139]Pham DL. Fuzzy clustering with spatial constraints. In:Image Processing 2002 Proceedings 2002 International Conference on:IEEE.2002,3(62), pp:65-68.
[140]Wang X, Wang Y, Wang L. Improving fuzzy c-means clustering based on feature-weight learning. Pattern Recognition Letters.2004,25(10), pp:123-132.
[141]Wu KL, Yang MS. Alternative c-means clustering algorithms. Pattern Recognition.2002,35(10), pp:2267-2278.
[142]Maji P, Pal SK. Rough set based generalized fuzzy c-means algorithm and quantitative indices. IEEE Transactions on Systems, Man, and Cybernetics, Part B.2007,37(6), pp:1529-1540.
[143]Mitra S, Banka H, Pedrycz W. Rough-fuzzy collaborative clustering. Systems, Man, and Cybernetics, Part B:Cybernetics, IEEE Transactions on.2006,36(4), pp:795-805.
[144]Pawlak Z. Rough Sets:Theoretical Aspects of Reasoning about Data. Volume 9 of System Theory, Knowledge Engineering and Problem Solving. In.:Kluwer Academic Publishers, Dordrecht, The Netherlands,1991.
[145]Han B, Wu TJ. Data mining in multisensor system based on rough set theory. In: American Control Conference,2001 Proceedings of the:IEEE.2001, pp: 4427-4431.
[146]Lingras P, Chen M, Miao D. Precision of Rough Set Clustering. In:Rough Sets and Current Trends in Computing.2008,9(8), pp:369-378.
[147]Lingras P, Yan R, Jain A. Web usage mining:Comparison of conventional, fuzzy, and rough set clustering. Computational Web Intelligence:Intelligent Technology for Web Applications, Springer.2004, pp:133-148.
[148]Dubois D, Prade H. Rough fuzzy sets and fuzzy rough sets. International Journal of General System.1990,17(2), pp:191-209.
[149]Mitra S, Banka H, Pedrycz W. Rough-fuzzy collaborative clustering. IEEE Transactions on Systems, Man, and Cybernetics, Part B:Cybernetics.2006, 36(4), pp:795-805.
[150]Maji P, Pal SK. Rough set based generalized fuzzy C-Means algorithm and quantitative indices. IEEE Transactions on Systems, Man, and Cybernetics, Part B:Cybernetics.2007,37(6), pp:1529-1540.
[151]Intan R, Mukaidono M. Generalized fuzzy rough sets by conditional probability relations. International Journal of Pattern Recognition and Artificial Intelligence.2002,16(7), pp:865-881.
[152]Nowak RD. Distributed EM algorithms for density estimation and clustering in sensor networks. IEEE Transactions on Signal Processing.2003,51(8), pp:2245-2253.
[153]Kamimura R. Cooperative information maximization with Gaussian activation functions for self-organizing maps. Neural Networks, IEEE Transactions on. 2006,17(4),pp:909-918.
[154]Safarinejadian B, Menhaj M, Karrari M. Distributed variational Bayesian algorithms for Gaussian mixtures in sensor networks. Signal Processing.2010, 90(4), pp:1197-1208.
[155]Gu D. Distributed EM algorithm for Gaussian mixtures in sensor networks. IEEE Transactions on Neural Networks.2008,19(7), pp:235-248.
[156]Safarinejadian B, Menhaj MB, Karrari M. Distributed Unsupervised Gaussian Mixture Learning for Density Estimation in Sensor Networks. Instrumentation and Measurement, IEEE Transactions on.2010,59(9), pp:2250-2260.
[157]Figueiredo M, Jain AK. Unsupervised learning of finite mixture models. Pattern Analysis and Machine Intelligence, IEEE Transactions on.2002,24(3), pp:381-396.
[158]McLachlan GJ, Peel D. Finite mixture models. New York:Wiley-Interscience, 2004.
[159]Figueiredo MAT, Jain AK. Unsupervised learning of finite mixture models. Pattern Analysis and Machine Intelligence, IEEE Transactions on.2002,24(3), pp:381-396.