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article · 2017

Feature Selection Algorithm Based on K-means Clustering

Xue Tang, Min Dong, Sheng Bi, Maofeng Pei, Dan Cao, Cheche Xie, Sunhuang Chi

Cited by 6Abstract and details only

Abstract

In order to improve the performance of the feature selection algorithm, a feature selection algorithm based on K-means clustering is designed. The algorithm makes use of the idea of K-means clustering based on cosine distance to cluster the features, so that the obtained feature subset has strong correlation and no redundancy. The experimental results show that the feature selection algorithm based on K-means clustering has high efficiency for classification tasks and has short running time, so the algorithm has strong practicability for feature selection.

Cite this paper

APA 7

Tang, X., Dong, M., Bi, S., Pei, M., Cao, D., Xie, C., & Chi, S. (2017). Feature Selection Algorithm Based on K-means Clustering. https://doi.org/10.1109/cyber.2017.8446108

MLA 9

Tang, Xue, et al. “Feature Selection Algorithm Based on K-means Clustering.” vol. 5, 2017, pp. 1522–27, https://doi.org/10.1109/cyber.2017.8446108.

Chicago

Tang, Xue, Min Dong, Sheng Bi, Maofeng Pei, Dan Cao, Cheche Xie, and Sunhuang Chi. 2017. “Feature Selection Algorithm Based on K-means Clustering.” https://doi.org/10.1109/cyber.2017.8446108.

Harvard

Tang, X. et al. (2017) ‘Feature Selection Algorithm Based on K-means Clustering’. Available at: https://doi.org/10.1109/cyber.2017.8446108.

BibTeX
@article{tang2017feature,
  title = {{Feature Selection Algorithm Based on K-means Clustering}},
  author = {Tang, Xue and Dong, Min and Bi, Sheng and Pei, Maofeng and Cao, Dan and Xie, Cheche and Chi, Sunhuang},
  volume = {5},
  pages = {1522--1527},
  year = {2017},
  doi = {10.1109/cyber.2017.8446108}
}

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