Name
Abstract | ||
|---|---|---|
Fuzzy clustering is an effective clustering approach which associates a data point with multiple clusters. Standard fuzzy clustering models like fuzzy c-means are based on minimizing the total cluster variation, which is defined as the sum of the distances between the data points and their corresponding cluster centers weighted by the membership degrees. In this paper, we propose a fuzzy minimax clustering model by minimizing the maximum value of the set of weighted cluster variations in such a way that they satisfy a prior distribution. We derive a necessary condition for the extremum point of the fuzzy minimax clustering model, and then design an iterative algorithm for solving the extremum point. Several numerical examples on comparing fuzzy c-means and fuzzy minimax clustering models are given, which demonstrate that the prior distribution improves the quality of the clustering results significantly. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1016/j.ins.2011.09.032 | Inf. Sci. |
Keywords | Field | DocType |
fuzzy clustering,effective clustering approach,extremum point,standard fuzzy clustering model,data point,clustering result,prior distribution,fuzzy c-means,fuzzy minimax,corresponding cluster center,image recognition | k-medians clustering,Fuzzy clustering,Mathematical optimization,CURE data clustering algorithm,Correlation clustering,Artificial intelligence,FLAME clustering,Cluster analysis,Fuzzy number,Machine learning,Mathematics,Single-linkage clustering | Journal |
Volume | Issue | ISSN |
186 | 1 | 0020-0255 |
Citations | PageRank | References |
12 | 0.55 | 16 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiang Li | 1 | 129 | 10.19 |
| Hau-San Wong | 2 | 1008 | 86.89 |
| Si Wu | 3 | 148 | 16.73 |