Name
Abstract | ||
|---|---|---|
Since a clustering algorithm can produce as many partitions as desired, one need to assess their quality in order to select the partition that most represents the structure in the data. This is the rationale for the cluster-validity (CV) problem and indices. This paper proposes a CV index for fuzzy-clustering algorithm, such as the fuzzy c-means (FCM) or its derivatives. Given a fuzzy partition, this new index uses global information and is based on more logical reasoning than geometrical features. Experimental results on artificial and benchmark datasets are given to demonstrate the performance of the proposed index, as compared with traditional and recent indices. |
Year | DOI | Venue |
|---|---|---|
2013 | null | WPMC |
Keywords | Field | DocType |
fuzzy set theory,fuzzy reasoning,pattern clustering,cluster validity (cv),fuzzy c-means,fuzzy-clustering algorithm,fuzzy-cluster analysis,fcm,benchmark dataset,artificial dataset,frequent pattern,cv index,fuzzy c-means (fcm),logical reasoning,cluster validity index,indexes | Data mining,Fuzzy clustering,Neuro-fuzzy,Defuzzification,Pattern recognition,Fuzzy classification,Computer science,Fuzzy set operations,Fuzzy logic,Fuzzy set,Artificial intelligence,Cluster analysis | Conference |
Volume | Issue | ISSN |
null | null | 1347-6890 |
Citations | PageRank | References |
1 | 0.35 | 4 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hongyan Cui | 1 | 53 | 20.53 |
| Kuo Zhang | 2 | 311 | 20.43 |
| Xu Huang | 3 | 133 | 39.14 |
| yunjie | 4 | 220 | 29.40 |