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Knowledge and Information Systems
Erschienen in: Knowledge and Information Systems 1/2015

01.10.2015 | Regular Paper

Synergy of two mutations based immune multi-objective automatic fuzzy clustering algorithm

verfasst von: Ruochen Liu, Lang Zhang, Bingjie Li, Yajuan Ma, Licheng Jiao

Erschienen in: Knowledge and Information Systems | Ausgabe 1/2015

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Abstract

In this paper, a synergy of two mutation based immune multi-objective automatic fuzzy clustering algorithm (STMIMAFC) is proposed for the task of automatically evolving the number of clusters as well as a proper partitioning of data set. In the proposed algorithm, firstly, two new mutation operators, which are designed for the different structures of chromosomes respectively, are cooperated with each other to generate the new individuals. Secondly, we propose an exponential function based compactness validity index. The proposed method has been extensively compared with a synergy of genetic algorithm and multi-objective differential evolution, multi-objective modified differential evolution based fuzzy clustering, multi-objective clustering with automatic \(k\)-determination over a test suit of several real life data sets and synthetic data sets. Experimental results indicate the superiority of the STMIMAFC over other three compared clustering algorithms on clustering accuracy and running time.
Vorheriger Artikel Enumeration of time series motifs of all lengths
Nächster Artikel Sliding windows over uncertain data streams

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Metadaten
Titel
Synergy of two mutations based immune multi-objective automatic fuzzy clustering algorithm
verfasst von
Ruochen Liu
Lang Zhang
Bingjie Li
Yajuan Ma
Licheng Jiao
Publikationsdatum
01.10.2015
Verlag
Springer London
Erschienen in
Knowledge and Information Systems / Ausgabe 1/2015
Print ISSN: 0219-1377
Elektronische ISSN: 0219-3116
DOI
https://doi.org/10.1007/s10115-014-0805-4

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