Automatic subspace clustering of high dimensional data for data mining applications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Fast algorithms for projected clustering
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Information Visualization and Visual Data Mining
IEEE Transactions on Visualization and Computer Graphics
SCHISM: A New Approach for Interesting Subspace Mining
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
P3C: A Robust Projected Clustering Algorithm
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
VISA: visual subspace clustering analysis
ACM SIGKDD Explorations Newsletter - Special issue on visual analytics
DUSC: Dimensionality Unbiased Subspace Clustering
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Surveying the complementary role of automatic data analysis and visualization in knowledge discovery
Proceedings of the ACM SIGKDD Workshop on Visual Analytics and Knowledge Discovery: Integrating Automated Analysis with Interactive Exploration
Evaluating clustering in subspace projections of high dimensional data
Proceedings of the VLDB Endowment
ACM SIGKDD Explorations Newsletter
SOREX: subspace outlier ranking exploration toolkit
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
INCONCO: interpretable clustering of numerical and categorical objects
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
A survey on enhanced subspace clustering
Data Mining and Knowledge Discovery
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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. In this work, we describe Morpheus, 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, Morpheus may serve as a teaching and exploration tool for the data mining community to visually assess different subspace clustering paradigms.