Sparse adaptive possibilistic clustering

In this paper a new sparse adaptive possibilistic clustering algorithm is presented. The algorithm exhibits high immunity to outliers and provides improved estimates of the cluster representatives by adjusting dynamically certain critical parameters. In addition, the proposed scheme manages - in principle - to estimate the actual number of clusters and by properly imposing sparsity, it becomes capable to deal well with closely located clusters of different densities. Extensive experimental results verify the previous statements.

[1]  J. A. Hartigan,et al.  A k-means clustering algorithm , 1979 .

[2]  Sergios Theodoridis,et al.  Pattern Recognition, Fourth Edition , 2008 .

[3]  Jian Yu,et al.  Alpha-Cut Implemented Fuzzy Clustering Algorithms and Switching Regressions , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[4]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[5]  Chuleerat Jaruskulchai,et al.  Possibilistic Exponential Fuzzy Clustering , 2013, Journal of Computer Science and Technology.

[6]  Mauro Barni,et al.  Comments on "A possibilistic approach to clustering" , 1996, IEEE Trans. Fuzzy Syst..

[7]  A. Householder.  The numerical treatment of a single nonlinear equation , 1970 .

[8]  Boris Mirkin,et al.  Clustering For Data Mining: A Data Recovery Approach (Chapman & Hall/Crc Computer Science) , 2005 .

[9]  James M. Keller,et al.  The possibilistic C-means algorithm: insights and recommendations , 1996, IEEE Trans. Fuzzy Syst..

[10]  Georgios B. Giannakis,et al.  Robust Clustering Using Outlier-Sparsity Regularization , 2011, IEEE Transactions on Signal Processing.

[11]  George F. Corliss.  Which Root Does the Bisection Algorithm Find , 1977 .

[12]  Konstantinos Koutroumbas,et al.  Adaptive possibilistic clustering , 2013, IEEE International Symposium on Signal Processing and Information Technology.

[13]  Yukihiro Hamasuna,et al.  On sparse possibilistic clustering with crispness — Classification function and sequential extraction , 2012, The 6th International Conference on Soft Computing and Intelligent Systems, and The 13th International Symposium on Advanced Intelligence Systems.

[14]  Kuo-Lung Wu,et al.  Unsupervised possibilistic clustering , 2006, Pattern Recognit..

[15]  James M. Keller,et al.  A possibilistic fuzzy c-means clustering algorithm , 2005, IEEE Transactions on Fuzzy Systems.

[16]  Sadaaki Miyamoto,et al.  Sparse Possibilistic Clustering with L1 Regularization , 2007, 2007 IEEE International Conference on Granular Computing (GRC 2007).

[17]  Jiang-She Zhang,et al.  Improved possibilistic C-means clustering algorithms , 2004, IEEE Trans. Fuzzy Syst..

[18]  Jianwen Fang,et al.  Predicting Penetration Across the Blood-Brain Barrier A Rough Set Approach , 2007 .