Paper Info
Title
Interactive clustering and merging with a new fuzzy expected value
Abstract
Major problems exist in both crisp and fuzzy clustering algorithms. The fuzzy c-means type of algorithms use weights determined by a power m of inverse distances that remains fixed over all iterations and over all clusters, even though smaller clusters should have a larger m. Our method uses a different “distance” for each cluster that changes over the early iterations to fit the clusters. Comparisons show improved results. We also address other perplexing problems in clustering: (i) find the optimal number K of clusters; (ii) assess the validity of a given clustering; (iii) prevent the selection of seed vectors as initial prototypes from affecting the clustering; (iv) prevent the order of merging from affecting the clustering; and (v) permit the clusters to form more natural shapes rather than forcing them into normed balls of the distance function. We employ a relatively large number K of uniformly randomly distributed seeds and then thin them to leave fewer uniformly distributed seeds. Next, the main loop iterates by assigning the feature vectors and computing new fuzzy prototypes. Our fuzzy merging then merges any clusters that are too close to each other. We use a modified Xie-Bene validity measure as the goodness of clustering measure for multiple values of K in a user-interaction approach where the user selects two parameters (for eliminating clusters and merging clusters after viewing the results thus far). The algorithm is compared with the fuzzy c-means on the iris data and on the Wisconsin breast cancer data.
Year
DOI
Venue
2002
10.1016/S0031-3203(01)00213-8
Pattern Recognition
Keywords
Field
DocType
Fuzzy clustering,Weighted fuzzy expected value,Fuzzy merging
k-medians clustering,Fuzzy clustering,CURE data clustering algorithm,Complete-linkage clustering,Correlation clustering,Pattern recognition,FLAME clustering,Artificial intelligence,Cluster analysis,Machine learning,Mathematics,Single-linkage clustering
Journal
Volume
Issue
ISSN
35
11
0031-3203
Citations 
PageRank 
References 
22
1.38
13
Authors
1
Name
Order
Citations
PageRank
Carl G. Looney119821.58