A Family of Fuzzy and Defuzzified c-Means Algorithms

This paper proposes a family of fuzzy and hard c-means algorithms. The hard clustering algorithms are derived from defuzzifying a generalized entropy-based fuzzy c-means whereby cluster volume size variables and covariance variables are introduced into hard clustering algorithms. Sequential algorith...

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Bibliographic Details
Main Authors: Miyamoto, S., Yasukochi, T., Inokuchi, R.
Format: Conference Proceeding
Language:English
Subjects:
Automation
Business
Clustering algorithms
Computational intelligence
Computational modeling
Covariance matrix
Intelligent agent
Internet
Minimization methods
Virtual colonoscopy
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Summary:This paper proposes a family of fuzzy and hard c-means algorithms. The hard clustering algorithms are derived from defuzzifying a generalized entropy-based fuzzy c-means whereby cluster volume size variables and covariance variables are introduced into hard clustering algorithms. Sequential algorithms are also derived by using advanced formulas of matrix multiplication. Crisp c-means as well as c-regression models are studied. Moreover effectiveness and efficiency of the proposed algorithms are compared using artificial as well as real data sets
DOI:10.1109/CIMCA.2005.1631463