Defuzzified Clustering Algorithms Derived from the Method of Entropy-Based Fuzzy c-Means
説明
A class of hard c-means algorithms is derived from 'defuzzifying' objective functions of the entropy-based fuzzy c-means and the KL-information based fuzzy c-means. Namely an entropy term is deleted from the objective functions while other parameters of cluster sizes and covariances are preserved. As a result, the objective function becomes linear with respect to the membership whereby a hard c-means algorithm is derived. Variations of the basic hard c-means algorithms are moreover proposed and reduction of computation using iterative matrix inversion is considered. Numerical examples are shown to compare results of proposed algorithms. Finally, a cluster validity measure is used whereby stability of clusters by different algorithms is compared.
収録刊行物
-
- 2005 IEEE International Conference on Systems, Man and Cybernetics
-
2005 IEEE International Conference on Systems, Man and Cybernetics 4 3221-3225, 2006-01-18
IEEE