Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Morpheus: interactive exploration of subspace clustering
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Pleiades: Subspace Clustering and Evaluation
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
EDSC: efficient density-based subspace clustering
Proceedings of the 17th ACM conference on Information and knowledge management
ACM Transactions on Knowledge Discovery from Data (TKDD)
Detection of orthogonal concepts in subspaces of high dimensional data
Proceedings of the 18th ACM conference on Information and knowledge management
Subspace and projected clustering: experimental evaluation and analysis
Knowledge and Information Systems
Evaluating clustering in subspace projections of high dimensional data
Proceedings of the VLDB Endowment
An extension of the PMML standard to subspace clustering models
Proceedings of the 2011 workshop on Predictive markup language modeling
Hybrid-LWM: A linear-model based hybrid clustering algorithm for supplier categorisation
International Journal of Systems, Control and Communications
Scalable density-based subspace clustering
Proceedings of the 20th ACM international conference on Information and knowledge management
External evaluation measures for subspace clustering
Proceedings of the 20th ACM international conference on Information and knowledge management
On the equivalence of PLSI and projected clustering
ACM SIGMOD Record
Finding multiple global linear correlations in sparse and noisy data sets
Knowledge-Based Systems
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Projected clustering has emerged as a possible solution to the challenges associated with clustering in high dimensional data. A projected cluster is a subset of points together with a subset of attributes, such that the cluster points project onto a small range of values in each of these attributes, and are uniformly distributed in the remaining attributes. Existing algorithms for projected clustering rely on parameters whose appropriate values are difficult to set by the user, or are unable to identify projected clusters with few relevant attributes. In this paper, we present a robust algorithm for projected clustering that can effectively discover projected clusters in the data while minimizing the number of parameters required as input. In contrast to all previous approaches, our algorithm can discover, under very general conditions, the true number of projected clusters. We show through an extensive experimental evaluation that our algorithm: (1) significantly outperforms existing algorithms for projected clustering in terms of accuracy; (2) is effective in detecting very low-dimensional projected clusters embedded in high dimensional spaces; (3) is effective in detecting clusters with varying orientation in their relevant subspaces; (4) is scalable with respect to large data sets and high number of dimensions.