改进小波聚类算法在QAR数据中的应用
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摘要
传统小波聚类算法标记满足密度阈值的连通单元为同一个簇,而不满足密度阈值的网格有可能存在属于簇的数据对象,数据的每维属性有时差距较大,不合适再划分均匀网格。为此,提出一种改进的小波聚类算法CWave Cluster,划分非均匀网格,进一步细化边界网格,对不满足密度阈值的网格进行处理,最终形成聚类。在指定的快速存取记录器(QAR)数据集上的实验结果表明,改进的小波聚类算法能根据数据特点划分网格,区分簇与簇的边界,有效解决QAR数据异常点检测问题。
Traditional wavelet clustering algorithm labels the communication unit satisfying density threshold as the same cluster,the mesh which does not meet the density threshold may have the data objects belonging to the cluster, and each dimension attribute of the data sometimes has a big gap,so that subdividing the mesh into uniform grid is not appropriate. Thus, an improved wavelet clustering algorithm is proposed. The method is used to divided the non-uniform grid, and refines further the boundary of the grid which does not satisfy the density threshold,and formats the clusters finally. By applying on the specified Quick Access Recorder( QAR) data sets,experimental results show that the improved wave cluster algorithm can effectiveing distinguish between cluster and boundary of the cluster, according to the characteristics of the data mesh,this method solves the question of the QAR data anomaly detection effectively.
Traditional wavelet clustering algorithm labels the communication unit satisfying density threshold as the same cluster,the mesh which does not meet the density threshold may have the data objects belonging to the cluster, and each dimension attribute of the data sometimes has a big gap,so that subdividing the mesh into uniform grid is not appropriate. Thus, an improved wavelet clustering algorithm is proposed. The method is used to divided the non-uniform grid, and refines further the boundary of the grid which does not satisfy the density threshold,and formats the clusters finally. By applying on the specified Quick Access Recorder( QAR) data sets,experimental results show that the improved wave cluster algorithm can effectiveing distinguish between cluster and boundary of the cluster, according to the characteristics of the data mesh,this method solves the question of the QAR data anomaly detection effectively.
引文
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[14]单世民,张宁,江贺,等.基于网格和密度的簇边缘精度增强聚类算法[J].计算机工程与应用,2008,44(8):143-146.
[15]余灿玲,王丽珍,张元武.基于网格密度方向的聚类簇边缘精度加强算法[J].计算机研究与发展,2010,15(5):816-822.
[2]SHEIKHOLESLAMI G,CHATTCRJCC S,ZHANG Aidong.Wave Cluster:A Multi-resolution Clustering Approach for Very Large Spatial Database[C]//Proceedings of International Conference on Very Large Data Base.New York,USA:ACM Press,1998:428-439.
[3]赵光嗣,李伟生.基于MPI的并行小波聚类算法在曙光TC1700上的实现[J].计算机应用,2006,26(3):645-646.
[4]方利民,林敏.小波聚类方法和近红外光谱技术用于药片种类判别[J].光谱学与光谱分析,2010,30(11):2958-2961.
[5]陈佐,谢赤,陈晖.基于小波聚类方法的股票收益序列时间模式与挖掘[J].系统工程,2005,23(11):102-107.
[6]MAHESHWARY P,SRIVASTAVA N.Wave Cluster for Remote Sensing Image Retrieval[J].International Journal on Computer Science and Engineering,2011,3(2):976-979.
[7]MICHAEL B.Mammographic Segmentation Using Wave Cluster[J].Algorithms,2012,5(3):318-329.
[8]LI Xingyi,LU Junyun,SHI Huaji,et al.An Approach for Treatment of the Incomplete Data Based on WaveCluter and Weighted 1-nearest Neighbor[C]//Proceedings of 2009 International Association of Computer Science and Information Technology Conference.Berlin,Germany:Springer,2009:3-8.
[9]ANGGRAINI E L,SUCIATI N,SUADI W.Parallel Computing of Wave Cluster Algorithm for Face Recognition Application[C]//Proceedings of International Conference on QIR.Washington D.C.,IEEE Press,2013:1195-1203.
[10]YILDIRIM A A,ZDOgˇAN C.Parallel Wavelet-based Clustering Algorithm on GPUs Using CUDA[J].Procedia Computer Science,2011,3(1):396-400.
[11]王治和,杨晏.基于双层网格和密度的数据流聚类算法[J].计算机工程,2014,40(4):146-150.
[12]林秀丹,毛国君.基于密度网格的分布式数据流聚类算法[J].计算机工程,2012,38(16):70-73.
[13]张书春,孙秀英.基于网格结构的CLARANS改进算法[J].计算机工程,2012,38(6):56-59.
[14]单世民,张宁,江贺,等.基于网格和密度的簇边缘精度增强聚类算法[J].计算机工程与应用,2008,44(8):143-146.
[15]余灿玲,王丽珍,张元武.基于网格密度方向的聚类簇边缘精度加强算法[J].计算机研究与发展,2010,15(5):816-822.