Journal ArticleDOI
A New Cluster Validity Index for Fuzzy Clustering
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TLDR
A new “Graded Distance index” (GD_index) is proposed for computing optimal number of fuzzy clusters for a given data set and the efficiency of this index is compared with well-known existing indices and tested on several data sets.About:
This article is published in IFAC Proceedings Volumes.The article was published on 2013-12-01. It has received 21 citations till now. The article focuses on the topics: Fuzzy clustering & Cluster analysis.read more
References
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Journal ArticleDOI
Data clustering: a review
TL;DR: An overview of pattern clustering methods from a statistical pattern recognition perspective is presented, with a goal of providing useful advice and references to fundamental concepts accessible to the broad community of clustering practitioners.
Journal ArticleDOI
A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters
TL;DR: Two fuzzy versions of the k-means optimal, least squared error partitioning problem are formulated for finite subsets X of a general inner product space; in both cases, the extremizing solutions are shown to be fixed points of a certain operator T on the class of fuzzy, k-partitions of X, and simple iteration of T provides an algorithm which has the descent property relative to the least squarederror criterion function.
A fuzzy relative of the isodata process and its use in detecting compact well-separated clusters
TL;DR: In this paper, two fuzzy versions of the k-means optimal, least squared error partitioning problem are formulated for finite subsets X of a general inner product space, and the extremizing solutions are shown to be fixed points of a certain operator T on the class of fuzzy, k-partitions of X, and simple iteration of T provides an algorithm which has the descent property relative to the LSE criterion function.
Journal ArticleDOI
A validity measure for fuzzy clustering
X.L. Xie,Gerardo Beni +1 more
TL;DR: The authors present a fuzzy validity criterion based on a validity function which identifies compact and separate fuzzy c-partitions without assumptions as to the number of substructures inherent in the data.
Journal ArticleDOI
Cluster Validity with Fuzzy Sets
TL;DR: This paper uses membership function matrices associated with fuzzy c-partitions of X, together with their values in the Euclidean (matrix) norm, to formulate an a posteriori method for evaluating algorithmically suggested clusterings of X.