Paper Info
Title
The fuzzy C-means algorithm with fuzzy P-mode prototypes for clustering objects having mixed features
Abstract
Frequency-based cluster prototypes have been used to cluster categorical objects, based on the simple matching dissimilarity measure. This paper introduces a new generalization called fuzzy p-mode prototype, of frequency-based prototypes. A fuzzy p-mode cluster prototype at a categorical feature is expressed as a list of p labels that have larger frequencies than others in the cluster. This paper also presents a new generalization of the fuzzy C-means clustering algorithm for the objects of mixed features. In the general fuzzy C-means clustering algorithm, any dissimilarity measures at the categorical feature level are assumed, not like other clustering algorithms that use the simple matching dissimilarity. The convergence of the general fuzzy C-means clustering algorithm under the optimization framework is proved. It is also explained through experiments over real object sets that the size of fuzzy p-mode prototypes and the fuzzification coefficients affect clustering performance.
Year
DOI
Venue
2009
10.1016/j.fss.2009.06.015
Fuzzy Sets and Systems
Keywords
Field
DocType
new generalization,clustering object,fuzzy c-means algorithm,frequency-based cluster prototype,mixed feature,fuzzy p-mode prototype,fuzzy p-mode cluster prototype,cluster categorical object,general fuzzy c-means,fuzzy c-means,clustering algorithm,categorical feature,categorical feature level,fuzzy clustering
Data mining,Fuzzy clustering,Fuzzy classification,Fuzzy set operations,Artificial intelligence,FLAME clustering,Fuzzy number,Cluster analysis,Single-linkage clustering,Pattern recognition,Correlation clustering,Machine learning,Mathematics
Journal
Volume
Issue
ISSN
160
24
Fuzzy Sets and Systems
Citations 
PageRank 
References 
15
0.65
8
Authors
2
Name
Order
Citations
PageRank
Mahnhoon Lee1162.36
W. Pedrycz2139661005.85