Inproceedings,
Morpheus: interactive exploration of subspace clustering
Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, page 1089--1092. New York, NY, USA, ACM, (2008)
DOI: 10.1145/1401890.1402026
Abstract
Data mining techniques extract interesting patterns out of large data resources. Meaningful visualization and interactive exploration of patterns are crucial for knowledge discovery. Visualization techniques exist for traditional clustering in low dimensional spaces. In high dimensional data, clusters typically only exist in subspace projections. This subspace clustering, however, lacks interactive visualization tools. Challenges arise from typically large result sets in different subspace projections that hinder comparability, visualization and understandability.</p> <p>In this work, we describe <i>Morpheus</i>, a tool that supports the knowledge discovery process through visualization and interactive exploration of subspace clusterings. Users may browse an overview of the entire subspace clustering, analyze subspace cluster characteristics in-depth and zoom into object groupings. Bracketing of different parameter settings enables users to immediately see the effects of parameters and to provide feedback to further improve the subspace clustering. Furthermore, <i>Morpheus</i> may serve as a teaching and exploration tool for the data mining community to visually assess different subspace clustering paradigms.
