Fuzzy k-means with variable weighting in high dimensional data analysis

Qiang Wang; Yunming Ye; Joshua Zhexue Huang

doi:10.1109/WAIM.2008.50

Fuzzy k-means with variable weighting in high dimensional data analysis

Qiang Wang^*
, Yunming Ye
, Joshua Zhexue Huang

^*Corresponding author for this work

Harbin Institute of Technology Shenzhen
The University of Hong Kong

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

22 Scopus citations

Abstract

This paper presents a comparison study of the fuzzy k-means algorithm and a new variant with variable weighting in clustering high dimensional data. The fuzzy k-means algorithm is effective in discovering the clusters with overlapping boundaries. However, this effectiveness can be handicapped in high dimensional data. The recent development of the k-means algorithm with automated variable weighting offers a new technique for dealing with high dimensional data that occurs in many new applications such as text mining and bioinformatics. In this paper, the variable weighting mechanism is incorporated in the fuzzy k-means algorithm to cluster high dimensional data with overlapping clusters. Experiments on real data sets have shown that the variable weighting fuzzy k-means produced better clustering results than the fuzzy k-means without variable weighting.

Original language	English
Title of host publication	Proceedings - The 9th International Conference on Web-Age Information Management, WAIM 2008
Pages	365-372
Number of pages	8
DOIs	https://doi.org/10.1109/WAIM.2008.50
State	Published - 2008
Externally published	Yes
Event	9th International Conference on Web-Age Information Management, WAIM 2008 - Zhangjiajie, China Duration: 20 Jul 2008 → 22 Jul 2008

Publication series

Name	Proceedings - The 9th International Conference on Web-Age Information Management, WAIM 2008

Conference

Conference	9th International Conference on Web-Age Information Management, WAIM 2008
Country/Territory	China
City	Zhangjiajie
Period	20/07/08 → 22/07/08

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10.1109/WAIM.2008.50

Cite this

Wang, Q., Ye, Y., & Huang, J. Z. (2008). Fuzzy k-means with variable weighting in high dimensional data analysis. In Proceedings - The 9th International Conference on Web-Age Information Management, WAIM 2008 (pp. 365-372). Article 4597036 (Proceedings - The 9th International Conference on Web-Age Information Management, WAIM 2008). https://doi.org/10.1109/WAIM.2008.50

@inproceedings{28bacea358d743fead0c91ad9a7e2efd,

title = "Fuzzy k-means with variable weighting in high dimensional data analysis",

abstract = "This paper presents a comparison study of the fuzzy k-means algorithm and a new variant with variable weighting in clustering high dimensional data. The fuzzy k-means algorithm is effective in discovering the clusters with overlapping boundaries. However, this effectiveness can be handicapped in high dimensional data. The recent development of the k-means algorithm with automated variable weighting offers a new technique for dealing with high dimensional data that occurs in many new applications such as text mining and bioinformatics. In this paper, the variable weighting mechanism is incorporated in the fuzzy k-means algorithm to cluster high dimensional data with overlapping clusters. Experiments on real data sets have shown that the variable weighting fuzzy k-means produced better clustering results than the fuzzy k-means without variable weighting.",

author = "Qiang Wang and Yunming Ye and Huang, \{Joshua Zhexue\}",

year = "2008",

doi = "10.1109/WAIM.2008.50",

language = "英语",

isbn = "9780769531854",

series = "Proceedings - The 9th International Conference on Web-Age Information Management, WAIM 2008",

pages = "365--372",

booktitle = "Proceedings - The 9th International Conference on Web-Age Information Management, WAIM 2008",

note = "9th International Conference on Web-Age Information Management, WAIM 2008 ; Conference date: 20-07-2008 Through 22-07-2008",

}

Wang, Q, Ye, Y & Huang, JZ 2008, Fuzzy k-means with variable weighting in high dimensional data analysis. in Proceedings - The 9th International Conference on Web-Age Information Management, WAIM 2008., 4597036, Proceedings - The 9th International Conference on Web-Age Information Management, WAIM 2008, pp. 365-372, 9th International Conference on Web-Age Information Management, WAIM 2008, Zhangjiajie, China, 20/07/08. https://doi.org/10.1109/WAIM.2008.50

Fuzzy k-means with variable weighting in high dimensional data analysis. / Wang, Qiang; Ye, Yunming; Huang, Joshua Zhexue.
Proceedings - The 9th International Conference on Web-Age Information Management, WAIM 2008. 2008. p. 365-372 4597036 (Proceedings - The 9th International Conference on Web-Age Information Management, WAIM 2008).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - This paper presents a comparison study of the fuzzy k-means algorithm and a new variant with variable weighting in clustering high dimensional data. The fuzzy k-means algorithm is effective in discovering the clusters with overlapping boundaries. However, this effectiveness can be handicapped in high dimensional data. The recent development of the k-means algorithm with automated variable weighting offers a new technique for dealing with high dimensional data that occurs in many new applications such as text mining and bioinformatics. In this paper, the variable weighting mechanism is incorporated in the fuzzy k-means algorithm to cluster high dimensional data with overlapping clusters. Experiments on real data sets have shown that the variable weighting fuzzy k-means produced better clustering results than the fuzzy k-means without variable weighting.

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DO - 10.1109/WAIM.2008.50

M3 - 会议稿件

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T2 - 9th International Conference on Web-Age Information Management, WAIM 2008

Y2 - 20 July 2008 through 22 July 2008

ER -

Fuzzy k-means with variable weighting in high dimensional data analysis

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