On the Selection of Parameter m in Fuzzy c-Means: A Computational Approach

Several clustering algorithms include one or more parameters to be fixed before its application. This is also the case of fuzzy c-means, one of the most well-known fuzzy clustering algorithms, where two parameters c and m are required. c corresponds to the number of clusters and m to the fuzziness of the solutions. The selection of these parameters is a critical issue because a bad selection can blur the clusters in the data. In this paper we propose a method for selecting an appropriate parameter m for fuzzy c-means based on an extensive computation. Our approach is based on the application of the clustering algorithm to several instantiations of the same data with different degrees of noise.

[1]  Josep Domingo-Ferrer,et al.  Ordinal, Continuous and Heterogeneous k-Anonymity Through Microaggregation , 2005, Data Mining and Knowledge Discovery.

[2]  M. Templ.  Statistical Disclosure Control for Microdata Using the R-Package sdcMicro , 2008, Trans. Data Priv..

[3]  Ali S. Hadi,et al.  Finding Groups in Data: An Introduction to Chster Analysis , 1991 .

[4]  Yaonan Wang,et al.  A Selection Model for Optimal Fuzzy Clustering Algorithm and Number of Clusters Based on Competitive Comprehensive Fuzzy Evaluation , 2009, IEEE Transactions on Fuzzy Systems.

[5]  James C. Bezdek,et al.  Visual Assessment of Clustering Tendency for Rectangular Dissimilarity Matrices , 2007, IEEE Transactions on Fuzzy Systems.

[6]  Hidetomo Ichihashi,et al.  Linear Fuzzy Clustering With Selection of Variables Using Graded Possibilistic Approach , 2007, IEEE Transactions on Fuzzy Systems.

[7]  J. C. Peters,et al.  Fuzzy Cluster Analysis : A New Method to Predict Future Cardiac Events in Patients With Positive Stress Tests , 1998 .

[8]  Lakhmi C. Jain,et al.  Introduction to Fuzzy Clustering , 2006 .

[9]  Jian Yu,et al.  A Generalized Fuzzy Clustering Regularization Model With Optimality Tests and Model Complexity Analysis , 2007, IEEE Transactions on Fuzzy Systems.

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

[11]  Miin-Shen Yang,et al.  Parameter selection for suppressed fuzzy c-means with an application to MRI segmentation , 2006, Pattern Recognit. Lett..

[12]  Josep Domingo-Ferrer,et al.  Practical Data-Oriented Microaggregation for Statistical Disclosure Control , 2002, IEEE Trans. Knowl. Data Eng..

[13]  Kitagawa Hiroyuki,et al.  Parameter Setting for a Clustering Method through an Analytical Study of Real Data , 2006 .

[14]  Vincent S. Tseng,et al.  A Novel Similarity-Based Fuzzy Clustering Algorithm by Integrating PCM and Mountain Method , 2007, IEEE Transactions on Fuzzy Systems.