Name
Abstract | ||
|---|---|---|
A fuzzy clustering algorithm based on differential evolution (FCDE) is presented in this paper in order to overcome the disadvantages of traditional fuzzy c-means algorithm (FCM). FCM is sensitive to initialization so that its search is easy to fall into a local optimum. The algorithm we proposed in this paper will avoid this problem and lead to global optimum. The experiments show that FCDE has better performance than FCM and is more efficient particularly when the number of dimension of data becomes large. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/ISDA.2008.270 | ISDA (1) |
Keywords | Field | DocType |
fuzzy clustering,fuzzy set theory,pattern clustering,traditional fuzzy c-means algorithm,evolutionary computation,differential evolution,better performance,fuzzy clustering algorithm,fuzzy c-means algorithm,fuzzy c-means,data clustering,classification algorithms,algorithm design and analysis,clustering algorithms,gallium | Data mining,Fuzzy clustering,Computer science,Fuzzy set,Artificial intelligence,Cluster analysis,Algorithm design,Pattern recognition,Local optimum,Fuzzy logic,Differential evolution,Initialization,Machine learning | Conference |
Volume | ISBN | Citations |
1 | 978-0-7695-3382-7 | 2 |
PageRank | References | Authors |
0.39 | 6 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yu-Cheng Kao | 1 | 159 | 7.96 |
| Jin-Cherng Lin | 2 | 136 | 16.88 |
| Shin-Chia Huang | 3 | 2 | 0.39 |