An improved initial cluster centers selection algorithm for k-means based on features correlative degree

被引：0
|
作者：
Chen, Xingshu [1 ]
Wu, Xiaosong [1 ]
Wang, Wenxian [1 ]
Wang, Haizhou [1 ]
机构：
[1] Network and Trusted Computing Inst., College of Computer Sci., Sichuan Univ., Chengdu
来源：
Sichuan Daxue Xuebao (Gongcheng Kexue Ban)/Journal of Sichuan University (Engineering Science Edition) | 2015年 / 47卷 / 01期
关键词：
Feature correlative degree; Initial cluster center; K-means; Text clustering;
D O I：
10.15961/j.jsuese.2015.01.002
中图分类号：
学科分类号：
摘要：
In order to solve the problem that K-means algorithms is highly sensitive to initial clusters centers in text clustering, an initial cluster center selection algorithm based on the correlative degree of features was proposed. Features with high correlative degree were chosen after reducing dimensions and a new dataset was created. Subsequently, a candidate initial cluster center set was constructed by merging the similar documents in the new dataset using “OR operation”. Finally, the best centers from the candidate dataset were obtained through computing document density and following the minimax principle. The results of five experimental datasets showed that most F-scores are more than 90%, and entropies are below 0.5. Comparison with the K-means algorithms of Mahout showed that the improved algorithm can choose higher quality centers and produce better clustering results. ©, 2015, Editorial Department of Journal of Sichuan University. All right reserved.
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页码：13 / 19
页数：6
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