成果信息
Hypergraph-Based Spectral Clustering for Categorical Data
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文献类型:会议论文
标题:Hypergraph-Based Spectral Clustering for Categorical Data
作者:Li, Yang[1];Guo, Chonghui[1]
机构:
年:2015
通讯作者:Li, Y (reprint author), Dalian Univ Technol, Inst Syst Engn, Dalian 116024, Peoples R China.
会议名称:International Conference on Advanced Computational Intelligence
会议论文集:2015 SEVENTH INTERNATIONAL CONFERENCE ON ADVANCED
页码范围:396-401
会议地点:Fujian, PEOPLES R CHINA
会议开始日期:2015-03-27
收录情况:EI(20160301829063)  CPCI-S(WOS:000380479600026)  Scopus(2-s2.0-84954322293)  
所属部门:经济管理学院
人气指数:109
浏览次数:109
语言:外文
摘要:Clustering categorical data has attracted much attention in recent years. In this paper, a hypergraph-based spectral clustering algorithm is proposed for categorical data. Firstly, we convert the categorical data to market basket type data by modeling each instance with categorical attributes as a transaction. By using an item set counting algorithm, a set of patterns (i.e. frequent item sets) can be discovered. Then we represent each transaction as a set of these patterns. In the hypergraph model, each transaction is represented as a vertex, and each pattern is regarded as a hyperedge. A hyperedge represents an affinity among subsets of transactions and the weight of the hyperedge reflects the strength of the affinity. At last a hypergraph-based spectral clustering algorithm is used to find the clustering results. Experimental results for selected UCI datasets show the effectiveness of the proposed algorithm.
全文链接:DOI百度学术
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