Paper Info
Title
Supervised Gravitational Clustering with Bipolar Fuzzification
Abstract
Data clustering is an important part of cluster analysis. Numerous semi-supervised or supervised clustering algorithms based on various theories have been developed, and new clustering algorithms continue to appear in the literature. The problem of common supervised clustering is to train a clustering algorithm to produce desirable clusters and complete clusters over datasets and learn how to cluster future sets of objects. In this paper, we have proposed an algorithm called Supervised Gravitational Clustering based on bipolar fuzzification. Traditional supervised clustering methods identify class-uniform clusters; but the offered method identifies class-multiform clusterswith high probability densities. For this aim we have proposed two approaches: common effect and maximal effect. The first, common effect approach, calculates total effect of all class-centers over searching point. Also, this approach is basis for mapping of novel method. The second, maximal effect approach, determines class-centers with the strongest effect over searching point.
Year
DOI
Venue
2008
10.1007/978-3-540-85984-0_80
ICIC (2)
Keywords
Field
DocType
bipolar fuzzification,supervised gravitational clustering,common supervised clustering,supervised clustering,common effect approach,maximal effect approach,data clustering,clustering algorithm,strongest effect,common effect,maximal effect,new clustering algorithm,k means,cluster analysis,probability density
Fuzzy clustering,Data mining,CURE data clustering algorithm,Computer science,Artificial intelligence,Cluster analysis,Single-linkage clustering,k-medians clustering,Canopy clustering algorithm,Pattern recognition,Correlation clustering,Constrained clustering,Machine learning
Conference
Volume
ISSN
Citations 
5227
0302-9743
0
PageRank 
References 
Authors
0.34
8
3
Name
Order
Citations
PageRank
Umut Orhan1608.66
Mahmut Hekim2433.89
Turgay Ibrikci31037.97