发表状态 | 已发表Published |
题名 | Subspace clustering of categorical and numerical data with an unknown number of clusters |
作者 | |
发表日期 | 2018-08-01 |
发表期刊 | IEEE Transactions on Neural Networks and Learning Systems
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ISSN/eISSN | 2162-237X |
卷号 | 29期号:8页码:3308-3325 |
摘要 | In clustering analysis, data attributes may have different contributions to the detection of various clusters. To solve this problem, the subspace clustering technique has been developed, which aims at grouping the data objects into clusters based on the subsets of attributes rather than the entire data space. However, the most existing subspace clustering methods are only applicable to either numerical or categorical data, but not both. This paper, therefore, studies the soft subspace clustering of data with both of the numerical and categorical attributes (also simply called mixed data for short). Specifically, an attribute-weighted clustering model based on the definition of object-cluster similarity is presented. Accordingly, a unified weighting scheme for the numerical and categorical attributes is proposed, which quantifies the attribute-to-cluster contribution by taking into account both of intercluster difference and intracluster similarity. Moreover, a rival penalized competitive learning mechanism is further introduced into the proposed soft subspace clustering algorithm so that the subspace cluster structure as well as the most appropriate number of clusters can be learned simultaneously in a single learning paradigm. In addition, an initialization-oriented method is also presented, which can effectively improve the stability and accuracy of k -means-type clustering methods on numerical, categorical, and mixed data. The experimental results on different benchmark data sets show the efficacy of the proposed approach. |
关键词 | Attribute weight categorical-and-numerical data initialization method number of clusters soft subspace clustering |
DOI | 10.1109/TNNLS.2017.2728138 |
URL | 查看来源 |
收录类别 | SCIE |
语种 | 英语English |
WOS研究方向 | Computer Science ; Engineering |
WOS类目 | Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic |
WOS记录号 | WOS:000439627700001 |
Scopus入藏号 | 2-s2.0-85029149478 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | https://repository.uic.edu.cn/handle/39GCC9TT/6299 |
专题 | 北师香港浸会大学 |
作者单位 | 1.College of Information Engineering,Shenzhen University,Shenzhen,518060,China 2.Department of Computer Science,Institute of Research and Continuing Education,Hong Kong Baptist University,Hong Kong 3.United International College,Beijing Normal University,Hong Kong Baptist University,Zhuhai,519085,China |
推荐引用方式 GB/T 7714 | Jia, Hong,Cheung, Yiu Ming. Subspace clustering of categorical and numerical data with an unknown number of clusters[J]. IEEE Transactions on Neural Networks and Learning Systems, 2018, 29(8): 3308-3325. |
APA | Jia, Hong, & Cheung, Yiu Ming. (2018). Subspace clustering of categorical and numerical data with an unknown number of clusters. IEEE Transactions on Neural Networks and Learning Systems, 29(8), 3308-3325. |
MLA | Jia, Hong,et al."Subspace clustering of categorical and numerical data with an unknown number of clusters". IEEE Transactions on Neural Networks and Learning Systems 29.8(2018): 3308-3325. |
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