Extended Fuzzy Clustering Algorithms
Fuzzy clustering is a widely applied method for obtaining fuzzy models from data. It has been applied successfully in various fields including finance and marketing. Despite the successful applications, there are a number of issues that must be dealt with in practical applications of fuzzy clustering algorithms. This technical report proposes two extensions to the objective function based fuzzy clustering for dealing with these issues. First, the (point) prototypes are extended to hypervolumes whose size is determined automatically from the data being clustered. These prototypes are shown to be less sensitive to a bias in the distribution of the data. Second, cluster merging by assessing the similarity among the clusters during optimization is introduced. Starting with an over-estimated number of clusters in the data, similar clusters are merged during clustering in order to obtain a suitable partitioning of the data. An adaptive threshold for merging is introduced. The proposed extensions are applied to Gustafson-Kessel and fuzzy c-means algorithms, and the resulting extended algorithms are given. The properties of the new algorithms are illustrated in various examples.
|Keywords||cluster merging, fuzzy clustering, similarity, volume prototypes|
|Publisher||Erasmus Research Institute of Management (ERIM)|
Kaymak, U., & Setnes, M.. (2000). Extended Fuzzy Clustering Algorithms (No. ERS-2000-51-LIS). Erasmus Research Institute of Management (ERIM). Retrieved from http://hdl.handle.net/1765/57