Computer Science ›› 2017, Vol. 44 ›› Issue (Z11): 457-459.doi: 10.11896/j.issn.1002-137X.2017.11A.097

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K-Means Clustering Algorithm Based on Initial Center Optimization and Feature Weighted

WANG Hong-jie and SHI Yan-wen   

  • Online:2018-12-01 Published:2018-12-01

Abstract: In order to improve the clustering accuracy of traditional K-Means clustering algorithm,an improved K-Means clustering algorithm based on initial center optimization and feature weighted was proposed.Firstly,the initial feature weight is obtained based on the contribution factor of sample feature for clustering,and a weighted distance metric is constructed.Next,the k initial clustering centers are obtained by using the proposed initial clustering center selection method,and the initial clustering is performed with the initial feature weight.Then,the feature weights are adjusted according to the clustering accuracy and the clustering process is performed again.The above process is repeated until the clustering accuracy is no longer changed,resulting in the final clustering result.The experimental results on the UCI database show that the algorithm has high clustering accuracy compared with the existing K-Means clustering algorithm.

Key words: K-Means clustering,Contribution factor,Feature weighted,Initial clustering center optimization

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