ACM Transactions on Knowledge Discovery from Data (TKDD)
Subspace and projected clustering: experimental evaluation and analysis
Knowledge and Information Systems
A new multiobjective clustering technique based on the concepts of stability and symmetry
Knowledge and Information Systems
Advancing data clustering via projective clustering ensembles
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Enhancing grid-density based clustering for high dimensional data
Journal of Systems and Software
Clustering very large multi-dimensional datasets with MapReduce
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
A weightless neural network-based approach for stream data clustering
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
Projective clustering ensembles
Data Mining and Knowledge Discovery
Semi-supervised projected model-based clustering
Data Mining and Knowledge Discovery
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Projected clustering partitions a data set into several disjoint clusters, plus outliers, so that each cluster exists in a subspace. Subspace clustering enumerates clusters of objects in all subspaces of a data set, and it tends to produce many overlapping clusters. Such algorithms have been extensively studied for numerical data, but only a few have been proposed for categorical data. Typical drawbacks of existing projected and subspace clustering algorithms for numerical or categorical data are that they rely on parameters whose appropriate values are difficult to set appropriately or that they are unable to identify projected clusters with few relevant attributes. We present P3C, a robust algorithm for projected clustering that can effectively discover projected clusters in the data while minimizing the number of required parameters. P3C does not need the number of projected clusters as input, and can discover, under very general conditions, the true number of projected clusters. P3C is effective in detecting very low-dimensional projected clusters embedded in high dimensional spaces. P3C positions itself between projected and subspace clustering in that it can compute both disjoint or overlapping clusters. P3C is the first projected clustering algorithm for both numerical and categorical data.