Multiple Kernel Based Collaborative Fuzzy Clustering Algorithm

详细信息    查看全文

• 关键词：Fuzzy clustering ; Collaborative clustering ; Fuzzy c ; means ; Multiple kernels
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9621
• 期：1
• 页码：585-594
• 全文大小：203 KB
• 参考文献：1.Kanungo, T., Mount, D.M., Netanyahu, N.S., Piatko, C.D., Silverman, R., Wu, A.Y.: An efficient k-Means clustering algorithm: analysis and implementation. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 881–893 (2002)CrossRef MATH
  2.Zalik, K.R.: An efficient k-means clustering algorithm. Pattern Recogn. Lett. 29, 1385–1391 (2008)CrossRef
  3.Bezdek, J.C., Ehrlich, R., Full, W.: FCM: the fuzzy c-Means clustering algorithm. Comput. Geosci. 10(2–3), 191–203 (1984)CrossRef
  4.Pedrycz, W.: Collaborative fuzzy clustering. Pattern Recogn. Lett. 23(14), 1675–1686 (2002)CrossRef MATH
  5.Pedrycz, W.: Collaborative clustering with the use of fuzzy C-Means and its quantification. Fuzzy Sets Syst. 159(18), 2399–2427 (2008)MathSciNet CrossRef MATH
  6.Pedrycz, W.: Collaborative and knowledge based fuzzy clustering. Int. J. Innovative Comput. Inf. control 3(1), 1–12 (2007)MATH
  7.Coletta, L.F.S., Vendramin, L., Hruschka, E.R., Campello, R.J.G.B., Pedrycz, W.: Collaborative fuzzy clustering algorithms: some refinements and design guidelines. IEEE Trans. Fuzzy Syst. 20(3), 444–462 (2012)CrossRef
  8.Pedrycz, W., Reformat, M.: Evolutionary fuzzy modeling. IEEE Trans. Fuzzy Syst. 11(5), 652–665 (2003)CrossRef
  9.Zhou, J., Chen, C.L.P., Chen, L., Li, H.X.: Collaborative fuzzy clustering algorithm in distributed network environments. IEEE Trans. Fuzzy Syst. 22(6), 1443–1456 (2014)CrossRef
  10.Jiang, Y., Chung, F.L., Wang, S., Deng, Z., Wang, J., Qian, P.: Collaborative fuzzy clustering from multiple weighted views. IEEE Trans. Cybern. 45(4), 688–701 (2015)CrossRef
  11.Prasad, M., Li, D.L., Liu, Y.T., Siana, L., Lin, C.T., Saxena, A.: A preprocessed induced partition matrix based collaborative fuzzy clustering for data analysis. In: IEEE International Conference on Fuzzy Systems, pp. 1553–1558 (2014)
  12.Yu, F., Tang, J., Cai, R.: Partially horizontal collaborative fuzzy C-Means. Int. J. Fuzzy Syst. 9(4), 198–204 (2007)MathSciNet
  13.Yu, F., Tang, J., Cai, R.: A necessary preprocessing in horizontal collaborative fuzzy clustering. In: International Conference on Granular Computing, pp. 399–404 (2007)
  14.Yu, F., Yu, S.: Prototypes-based horizontal collaborative fuzzy clustering. In: Web Mining and Web-based Application, pp. 66–69 (2009)
  15.Falcon, R., Depaire, B., Vanhoof, K., Abraham, A.: Towards a suitable reconciliation of the findings in collaborative fuzzy clustering. In: ISDA International Conference, vol. 4, pp. 652–657 (2008)
  16.Mitra, S., Banka, H., Pedrycz, W.: Roughfuzzy collaborative clustering. IEEE Trans.Syst. Man Cybern. Part B Cybern. 36(4), 795–805 (2006)CrossRef
  17.Girolami, M.: Mercer kernel-based clustering in feature space. IEEE Trans. Neural Networks 13, 780–784 (2002)CrossRef
  18.Graves, D., Pedrycz, W.: Kernel-based fuzzy clustering and fuzzy clustering: a comparative experimental study. Fuzzy Sets Syst. 161, 522–543 (2010)MathSciNet CrossRef
  19.Sonnenburg, S., Ratsch, G., Schafer, C., Scholkopf, B.: Large scale multiple kernel learning. J. Mach. Learn. Res. 7, 1531–1565 (2006)MathSciNet MATH
  20.Zhanga, D.-Q., Chen, S.-C.: A novel kernelized fuzzy C-means algorithm with application in medical image segmentation. Artif. Intell. Med. 32(1), 37–50 (2004)CrossRef
  21.Yang, M.-S., Tsai, H.-S.: A gaussian kernel-based fuzzy c-means algorithm with a spatial bias correction. Pattern Recogn. Lett. 29, 1713–1725 (2008)CrossRef
  22.Chen, L., Chen, C.L.P., Lu, M.: A multiple-kernel fuzzy C-Means algorithm for image segmentation. Syst. Man Cybern. Part B Cybern. 41(5), 1263–1274 (2011)CrossRef
  23.Huang, H., Chuang, Y.Y., Chen, C.S.: Multiple kernel fuzzy clustering. IEEE Trans. on Fuzzy Syst. 20(1), 120–134 (2011)CrossRef
  24.Dang, T.H., Ngo, L.T., Pedrycz, W.: Interval type-2 fuzzy C-Means approach to collaborative clustering. In: The IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2015) (2015)
  
• 作者单位：Trong Hop Dang (17) (18)
  Long Thanh Ngo (17)
  Wiltold Pedrycz (19)
  
  17. Department of Information Systems, Le Quy Don Technical University, Hanoi, Vietnam
  18. Hanoi University of Industry, Bac Tu Liem, Hanoi, Vietnam
  19. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada
  
• 丛书名：Intelligent Information and Database Systems
• ISBN：978-3-662-49381-6
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

Cluster is found as one of the best useful tools for data analysis, data mining, and pattern recognition. The FCM algorithm and its variants algorithms has been extensively used in problems of clustering or collaborative clustering. In this paper, we present a novel method involving multiple kernel technique and FCM for collaborative clustering problem. These method endowed with multiple kernel technique which transform implicitly the feature space of input data into a higher dimensional via a non linear map, which increases greatly possibility of linear separability of the patterns when the data structure of input patterns is non-spherical and complex. To evaluate the proposed method, we use the criteria of fuzzy silhouette, a sum of squared error and classification rate to show the performance of the algorithms.