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A possibilistic approach to clustering
The clustering problem is cast in the framework of possibility theory. Expand
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The possibilistic C-means algorithm: insights and recommendations
The possibilistic C-means algorithm (PCM) was proposed to address the drawbacks associated with the constrained memberships used in algorithms such as the fuzzy C-Means. Expand
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Robust clustering methods: a unified view
Clustering methods need to be robust if they are to be useful in practice. Expand
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Clustering by competitive agglomeration
We present a new clustering algorithm called Competitive Agglomeration (CA), which minimizes the objective function that incorporates the advantages of both hierarchical and partitional clustering and produces a sequence of partitions with a decreasing number of clusters. Expand
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Low-complexity fuzzy relational clustering algorithms for Web mining
This paper presents new algorithms-fuzzy c-medoids (FCMdd) and robust fuzzy c-Medoids (RFCMdd)-for fuzzy clustering of relational data. Expand
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A Robust Competitive Clustering Algorithm With Applications in Computer Vision
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. Expand
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Fuzzy co-clustering of documents and keywords
We modify the FCCM algorithm so that it can be used to cluster large text corpora. Expand
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An overview of membership function generation techniques for pattern recognition
We provide a general overview of several methods for generating membership functions for fuzzy pattern recognition applications, and discuss the suitability and applicability of these membership function generation techniques to particular situations. Expand
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The fuzzy c spherical shells algorithm: A new approach
The fuzzy c spherical shells clustering algorithm is specially designed to search for clusters that can be described by circular arcs or hyperspheres. Expand
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A hierarchical monothetic document clustering algorithm for summarization and browsing search results
We propose a new hierarchical monothetic clustering algorithm to build a topic hierarchy for a collection of search results retrieved in response to a query. Expand
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