Outlier factor based partitional clustering analysis with constraints discovery and representative objects generation
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- 作者：Zonglin Ye silitater@stu.xjtu.edu.cn" class="auth_mail" title="E-mail the corresponding authorAuthor Vitae ; Hui Cao ; huicao@mail.xjtu.edu.cn" class="auth_mail" title="E-mail the corresponding authorAuthor Vitae ; Yanbin Zhang ybzhang@mail.xjtu.edu.cn" class="auth_mail" title="E-mail the corresponding authorAuthor Vitae ; Lixin Jia lxjia@mail.xjtu.edu.cn" class="auth_mail" title="E-mail the corresponding authorAuthor Vitae
- 关键词：Clustering algorithm ; K-means ; Outlier factor ; Constraints ; Discovery ; Representative objects
- 刊名：Neurocomputing
- 出版年：2016
- 出版时间：15 January 2016
- 年：2016
- 卷：173
- 期：part_P3
- 页码：1538-1553
- 全文大小：946 K
文摘
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New method to combine outlier factor with k-means algorithm, and local information is effectively used.
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The proposed method is compared with two classical constrained k-means algorithms.
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Besides UCI datasets, a field dataset from a ball mill pulverizing system is used in the experiment.
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An outlier factor is proposed in the paper and is compared with LOF and COF in the experiment.
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The impact of the parameter of the outlier factors are tested and discussed in detail in the experiment.