|Title:||Mining Significant Subspaces||Other Titles:||重要子空間之資料探勘||Authors:||ANTHONY J. T. LEE
|Issue Date:||Dec-2010||Journal Volume:||17卷專刊||Start page/Pages:||27-49||Source:||資訊管理學報||Abstract:||
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.
|Appears in Collections:||資訊管理學系|
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