NDLI: Principal Component Analysis based Feature Selection for clustering

Content Provider	IEEE Xplore Digital Library
Author	Jun-Ling Xu Bao-Wen Xu Wei-Feng Zhang Zi-Feng Cui
Copyright Year	2008
Description	Author affiliation: Sch. of Comput. Sci. & Eng., Southeast Univ., Nanjing (Jun-Ling Xu; Bao-Wen Xu)
Abstract	Feature extraction (FE) methods have been proved to be very effective for dimension reduction, but the features attained are meaningless. In order to exploit the effectiveness of FE methods to support feature selection (FS), this paper proposed a new FS approach for clustering based on principal component analysis (PCA) called PS. It first uses PCA to transform the data from original feature space into a new feature space whose features are linear combination of the original ones, and then evaluates the importance of the original features based on the newly generated features and the feature importance measure proposed in this paper, finally selects features incrementally according to their importance to improve the performance of the clustering algorithm. Experiment is carried out on several popular data sets and the results show the advantages of the proposed approach.
Starting Page	460
Ending Page	465
File Size	307030
Page Count	6
File Format	PDF
ISBN	9781424420957
DOI	10.1109/ICMLC.2008.4620449
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-07-12
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Principal component analysis Clustering algorithms Algorithm design and analysis Feature extraction Entropy Classification algorithms Machine learning Clustering Feature selection
Content Type	Text
Resource Type	Article

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