Automatic subspace clustering of high dimensional data for data mining applications

Data mining applications place special requirements on clus-tering algorithms including: the ability to find clusters em-bedded in subspaces of high dimensional data, scalability, end-user comprehensibility of the results, non-presumption of any canonical data distribution, and insensitivity to the order of input records. We present CLIQUE, a clustering al-gorithm that satisfies each of these requirements. CLIQUE identifies dense clusters in subspaces of maximum dimen-sionality. It generates cluster descriptions in the form of DNF expressions that are minimized for ease of comprehen-sion. It produces identical results irrespective of the order in which input records are presented and does not presume any specific mathematical form for data distribution. Through experiments, we show that CLIQUE efficiently finds accu-rate clusters in large high dimensional datasets.