A Self-organized Network for Data Clustering

In this paper, a dynamical model for data clustering is proposed. This approach employs a network consisting of interacting elements with each representing an attribute vector of input data and receiving attractions from other elements within a certain region. Those attractions, determined by a pred...

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Bibliographic Details
Published inAdvances in Natural Computation pp. 1189 - 1198
Main Authors Zhao, Liang, Damiance, Antonio P. G., Carvalho, Andre C. P. L. F.
Format Book Chapter Conference Proceeding
LanguageEnglish
Published Berlin, Heidelberg Springer Berlin Heidelberg 2005
Springer
SeriesLecture Notes in Computer Science
Subjects
Applied sciences
Artificial intelligence
Computer science; control theory; systems
Exact sciences and technology
Pattern recognition. Digital image processing. Computational geometry
Cluster analysis
Attraction
Self organization
Similarity
Computer simulation
Image databank
Classification
Data models
Metric
Data mining
Modeling
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Summary:In this paper, a dynamical model for data clustering is proposed. This approach employs a network consisting of interacting elements with each representing an attribute vector of input data and receiving attractions from other elements within a certain region. Those attractions, determined by a predefined similarity measure, drive the elements to converge to their corresponding cluster center. With this model, neither the number of data clusters nor the initial guessing of cluster centers is required. Computer simulations for clustering of real images and Iris data set are performed. The results obtained so far are very promising.
ISBN:3540283234
9783540283232
ISSN:0302-9743
1611-3349
DOI:10.1007/11539087_158