https://scholars.lib.ntu.edu.tw/handle/123456789/415139
標題: | Mining Significant Subspaces | 其他標題: | 重要子空間之資料探勘 | 作者: | ANTHONY J. T. LEE Ming-Chih Lin Yun-Ru Wang Kuo-Tay Chen |
公開日期: | 十二月-2010 | 卷: | 17卷專刊 | 起(迄)頁: | 27-49 | 來源出版物: | 資訊管理學報 | 摘要: | As both the number of dimensions increases, existing clustering methods in full feature space are not appropriate to cluster data in databases. Thus, the subspace clustering has attracted more and more attention recently. In this paper, we propose a novel method to mine significant subspaces from all frequent subspaces, where a subspace is frequent if it contains enough data points. The proposed method consists of three phases. First, we generate all frequent 2-dimensional subspaces. Second, we recursively combine frequent k-dimensional subspaces to generate frequent (k+1)-dimensional subspaces, k≥2. Finally, we adopt a greedy algorithm to summarize the frequent subspaces generated and select the significant ones. The experimental results show that the proposed method has better quality and coverage than DUSC, and better quality than FIRES. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/415139 | DOI: | 10.6382/JIM.201012.0027 |
顯示於: | 資訊管理學系 |
在 IR 系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。