Rough-fuzzy collaborative clustering.

Sushmita Mitra; Haider Banka; Witold Pedrycz

doi:10.1109/tsmcb.2005.863371

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PubMed · 16903365

Rough-fuzzy collaborative clustering.

Sushmita Mitra·Haider Banka·Witold Pedrycz

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Abstract

In this study, we introduce a novel clustering architecture, in which several subsets of patterns can be processed together with an objective of finding a common structure. The structure revealed at the global level is determined by exchanging prototypes of the subsets of data and by moving prototypes of the corresponding clusters toward each other. Thereby, the required communication links are established at the level of cluster prototypes and partition matrices, without hampering the security concerns. A detailed clustering algorithm is developed by integrating the advantages of both fuzzy sets and rough sets, and a measure of quantitative analysis of the experimental results is provided for synthetic and real-world data.

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Algorithms Artificial Intelligence Cluster Analysis Computer Simulation Cooperative Behavior Fuzzy Logic Models, Statistical Pattern Recognition, Automated Journal Article Research Support, Non-U.S. Gov't

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Sushmita Mitra, Haider Banka, Witold Pedrycz. 2006. Rough-fuzzy collaborative clustering.. https://doi.org/10.1109/tsmcb.2005.863371

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