/spl delta/-clusters: capturing subspace correlation in a large data set

By Jiong Yang, Wei Wang, Haixun Wang, Philip S. Yu. Proceedings / International Conference on Data Engineering, 2002

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Clustering by pattern similarity in large data sets (2002, methodology): “Yang et al [20] proposed a move-based algorithm to ﬁnd biclusters more eﬃciently.”
Subspace clustering for high dimensional data: a review (2004, methodology): “The δ-Clusters algorithm uses a distance measure that attempts to capture thecoherence exhibited by subset of instances on subset of attributes [75].”
Biclustering in data mining (2008, methodology): “Yang et al. generalized the deﬁnition of residue used in the node-deletion algorithm to allow missing data entries (i.e., some a ij may be unknown) [22,23].”
Cluster analysis for gene expression data: a survey (2004, methodology): “Yang at al. [ 74 ] present a subspace clustering method named “ -clusters” to capture embedded”
An Entropy Weighting k-Means Algorithm for Subspace Clustering of High-Dimensional Sparse Data (2007, methodology): “Using PROCLUS, the user needs to specify the number of clusters k and the average number of cluster dimensions l. Other top-down methods include ORCLUS [8], FINDIT [27], and � -Clusters [ 28 ].”