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International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 1/2013

01.02.2013 | Original Article

Spectral co-clustering documents and words using fuzzy K-harmonic means

verfasst von: Na Liu, Fei Chen, Mingyu Lu

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 1/2013

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Abstract

This paper analyzes the main steps of spectral co-clustering documents and words, finds out its cause of sensitivity to input order, and presents a modified method of spectral co-clustering documents and words based on fuzzy K-harmonic means. This method consists of two steps. The first step constructs Laplacian matrix which is insensitive to input order. The second step exploits fuzzy K-harmonic means algorithm instead of K-means algorithm to obtain clustering results. Fuzzy K-harmonic means algorithm uses fuzzy weight distance while calculating the distance between each data points and cluster centers. The experiments show that the proposed method not only is insensitive to input order, but also can improve the accuracy and robustness of clustering results.
Vorheriger Artikel Capacitated two-stage facility location problem with fuzzy costs and demands

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Metadaten
Titel
Spectral co-clustering documents and words using fuzzy K-harmonic means
verfasst von
Na Liu
Fei Chen
Mingyu Lu
Publikationsdatum
01.02.2013
Verlag
Springer-Verlag
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 1/2013
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-012-0077-9

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