A Hierarchical Clustering for Categorical Data Based on Holo-entropy

摘要：

　　High dimensional data clustering is a difficult task in clustering analysis.Subspace clustering is an effective approach.The principle of subspace clustering is to maximize the retention of the original data information while searching for the minimal size of subspace for cluster representation.Based on information entropy and Holo-entropy, we propose an adaptive high dimensional weighted subspace clustering algorithm.The algorithm employs information entropy to extract the feature subspace, uses class compactness which binding Holo-entropy with weight in subspace for sub-clusters merging instead of the traditional similarity measurement method, and it selects the most compacted two sub-clusters to merge to achieve the maximum degree clustering effect.The algorithm is tested on nine UCi dataset, and compared with other algorithms.Our algorithm is better in both efficiency and accuracy than the other existing algorithms and has high reproducibility.

关键词： Hierarchical Clustering Holo-entropy Subspace Categorical Data

作者: Haojun Sun Rongbo Chen Shulin Jin Yong Qin

作者单位: Department of Computer Science Shantou University Shantou, China State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing

会议类型: 国际会议

会议名称: The 12th Web Information System and Application Conference第十二届全国Web信息系统及其应用学术会议(WISA2015)、全国第十次语义Web 与本体论学术研讨会(SWON2015)、全国第九次电子政务技术及应用学术研讨会(EGTA2015)

会议地点: 济南

会议语种:英文

页码: 269-274

在线出版日期: 2015-09-11（万方平台首次上网日期，不代表论文的发表时间）