Subspace clustering of high dimensional data
Carlotta Domeniconi, Dimitris Papadopoulos, et al.
SDM 2004
Data mining applications place special requirements on clustering algorithms including: the ability to find clusters embedded 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 algorithm that satisfies each of these requirements. CLIQUE identifies dense clusters in subspaces of maximum dimensionality. It generates cluster descriptions in the form of DNF expressions that are minimized for ease of comprehension. 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 accurate clusters in large high dimensional datasets. © 1998 ACM.
Carlotta Domeniconi, Dimitris Papadopoulos, et al.
SDM 2004
Rakesh Agrawal, Tomasz Imieliński, et al.
SIGMOD Record
Rakesh Agrawal, Ameet Kini, et al.
SIGMOD 2004
Demetrios Zeinalipour-Yazti, Christos Laoudias, et al.
IEEE TKDE