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An Incremental Clustering with Attribute Unbalance Considered for Categorical Data

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机构: [1]School of Information Science and Technology, Sun Yat-sen University, Guangzhou, 510275, China [2]The 2nd Affiliated Hospital, Guangzhou University of Chinese Medicine, Guangzhou, 510120, China
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关键词: incremental clustering attribute unbalance categorical data

摘要:
Clustering analysis is an important technique used in many fields. But traditional clustering algorithms generally deal with numeric data. While clustering categorical data have always attracted researchers' attentions because of their prevalence in real life. This paper analyses limitations of the categorical clustering algorithms proposed. Based on two observations, a new similarity measure is proposed for categorical data which considers the unbalance of attributes. As the data are getting much larger and more dynamic, incremental is an important quality of good clustering algorithms. The clustering algorithm present is an incremental with linear computing complexity. The experiment results indicate that it outperforms other categorical clustering algorithms referred in the paper.

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第一作者机构: [1]School of Information Science and Technology, Sun Yat-sen University, Guangzhou, 510275, China
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