Genetic-based K-means algorithm for selection of feature variables
Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45) › 32_Refereed conference paper (with ISBN/ISSN) › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
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Title of host publication | Proceedings - International Conference on Pattern Recognition |
Pages | 744-747 |
Volume | 2 |
Publication status | Published - 2006 |
Publication series
Name | |
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Volume | 2 |
ISSN (Print) | 1051-4651 |
Conference
Title | 18th International Conference on Pattern Recognition, ICPR 2006 |
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Place | China |
City | Hong Kong |
Period | 20 - 24 August 2006 |
Link(s)
Abstract
This paper proposes a genetic-based K-means(GK) algorithm for selection of the k value and selection of feature variables by minimizing an associated objective function. The algorithm combines the advantage of genetic algorithm(GA) and K-means to search the subspace thoroughly. Therefore, our algorithm converges globally. A weighting function is then introduced to initialize the parameters of the algorithm. The experiments on a synthetic dataset and a real dataset shows that (i) GK outperforms K-means since GK achieves the minimal value of the objective function and (ii) GK with the weighting function performs better than GK. © 2006 IEEE.
Citation Format(s)
Genetic-based K-means algorithm for selection of feature variables. / Yu, Zhiwen; Wong, Hau-San.
Proceedings - International Conference on Pattern Recognition. Vol. 2 2006. p. 744-747 1699312.Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45) › 32_Refereed conference paper (with ISBN/ISSN) › peer-review