Fuzzy C-means based on automated variable feature weighting
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Authors
Nazari, Mousa
Shanbehzadeh, Jamshid
Sarrafzadeh, Hossein
Shanbehzadeh, Jamshid
Sarrafzadeh, Hossein
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Grantor
Date
2013
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Type
Conference Contribution - Paper in Published Proceedings
Ngā Upoko Tukutuku (Māori subject headings)
Keyword
fuzzy clustering
fuzzy C-means
feature weighting
weighted fuzzy C-means
fuzzy C-means
feature weighting
weighted fuzzy C-means
ANZSRC Field of Research Code (2020)
Citation
Nazari, M., Shanbehzadeh, J., and Sarrafzadeh, A. (2013). Fuzzy C-means based on automated variable feature weighting. Proceedings of the International MultiConference of Engineers and Computer Scientists, 2013(Ed.), Hong Kong,1. 25-29.
Abstract
Fuzzy C-means (FCM) is a powerful clustering algorithm and has been introduced to overcome the crisp definition of similarity and clusters. FCM ignores the importance of features in the clustering process. This affects its authenticity and accuracy. We can overcome this problem by appropriately assigning weights to features according to their clustering importance. This paper, proposes an improved FCM algorithm based on the method proposed by Huang by automated feature weighting. The simulation results on several UCI databases show that the proposed algorithm exhibits better performance than FCM.
Publisher
Newswood Ltd.
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Newswood Ltd.
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