Relational partitioning fuzzy clustering algorithms based on multiple dissimilarity matrices

This paper introduces fuzzy clustering algorithms that can partition objects taking into account simultaneously their relational descriptions given by multiple dissimilarity matrices. The aim is to obtain a collaborative role of the different dissimilarity matrices to get a final consensus partition...

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Vydáno v:Fuzzy sets and systems Ročník 215; s. 1 - 28
Hlavní autoři: de Carvalho, Francisco de A.T., Lechevallier, Yves, de Melo, Filipe M.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 16.03.2013
Elsevier
Témata:
Algorithms
Clusters
Collaborative clustering
Fuzzy
Fuzzy clustering
Fuzzy logic
Fuzzy medoids
Fuzzy set theory
Machine Learning
Mathematical analysis
Matrices
Matrix methods
Multiple dissimilarity matrices
Relational data
Relevance weight
Statistics
Collaborative clustering
Fuzzy medoids
Fuzzy clustering
Relevance weight
Relational data
Multiple dissimilarity matrices
ISSN:0165-0114, 1872-6801
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Shrnutí:This paper introduces fuzzy clustering algorithms that can partition objects taking into account simultaneously their relational descriptions given by multiple dissimilarity matrices. The aim is to obtain a collaborative role of the different dissimilarity matrices to get a final consensus partition. These matrices can be obtained using different sets of variables and dissimilarity functions. These algorithms are designed to furnish a partition and a prototype for each fuzzy cluster as well as to learn a relevance weight for each dissimilarity matrix by optimizing an adequacy criterion that measures the fit between the fuzzy clusters and their representatives. These relevance weights change at each algorithm iteration and can either be the same for all fuzzy clusters or different from one fuzzy cluster to another. Experiments with real-valued data sets from the UCI Machine Learning Repository as well as with interval-valued and histogram-valued data sets show the usefulness of the proposed fuzzy clustering algorithms.
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ISSN:0165-0114
1872-6801
DOI:10.1016/j.fss.2012.09.011