00000cab a22000003a 4500 UP-99796217611077290 Buklod 20231008001308.0 m |o d | ta 140124s xx d | ||r ||||| || DENGII eng Tatu, Andrada ClustNails visual analysis of subspace clusters. August 2012 p.419-428 Subspace clustering addresses an important problem in clustering multi-dimensional data. In sparse multi-dimensional data, many dimensions are irrelevant and obscure the cluster boundaries. Subspace clustering helps by mining the clusters present in only locally relevant subsets of dimensions. However, understanding the result of subspace clustering by analysts is not trivial. In addition to the grouping information, relevant sets of dimensions and overlaps between groups, both in terms of dimensions and records, need to be analyzed. We introduce a visual subspace cluster analysis system called ClustNails. It integrates several novel visualization techniques with various user interaction facilities to support navigating and interpreting the result of subspace clustering. We demonstrate the effectiveness of the proposed system by applying it to the analysis of real world data and comparing it with existing visual subspace cluster analysis systems. Algorithm design and analysis. Pixel-based techniques. Clustering algorithms. Zhang, Leishi. Bertini, Enrico. Schreck, Tobias. Keim, Daniel. Bremm, Sebastian. Von Landesberger, Tatiana. Tsinghua Science and Technology 17, 4 (Aug. 2012). FO Article