R
Raghu Krishnapuram
Researcher at Indian Institute of Science
Publications - 139
Citations - 10422
Raghu Krishnapuram is an academic researcher from Indian Institute of Science. The author has contributed to research in topics: Cluster analysis & Fuzzy logic. The author has an hindex of 42, co-authored 139 publications receiving 10064 citations. Previous affiliations of Raghu Krishnapuram include University of Missouri & Indian Institutes of Technology.
Papers
More filters
Journal ArticleDOI
A possibilistic approach to clustering
TL;DR: An appropriate objective function whose minimum will characterize a good possibilistic partition of the data is constructed, and the membership and prototype update equations are derived from necessary conditions for minimization of the criterion function.
Journal ArticleDOI
Robust clustering methods: a unified view
TL;DR: This paper analyzes several popular robust clustering methods and concludes that they have much in common, establishing a connection between fuzzy set theory and robust statistics, and pointing out the similarities between robust clusters methods and statistical methods.
Journal ArticleDOI
The possibilistic C-means algorithm: insights and recommendations
TL;DR: The underlying principles of the PCM and the possibilistic approach, in general are examined and the results reported by Barni et al. are interpreted in the light of their findings.
Journal ArticleDOI
A robust competitive clustering algorithm with applications in computer vision
H. Frigui,Raghu Krishnapuram +1 more
TL;DR: This paper addresses three major issues associated with conventional partitional clustering, namely, sensitivity to initialization, difficulty in determining the number of clusters, and sensitivity to noise and outliers with the proposed robust competitive agglomeration (RCA).
Journal ArticleDOI
Low-complexity fuzzy relational clustering algorithms for Web mining
TL;DR: A comparison of FCMdd with the well-known relational fuzzy c-means algorithm (RFCM) shows thatFCMdd is more efficient, and several applications of these algorithms to Web mining, including Web document clustering, snippet clusters, and Web access log analysis are presented.