Automatic Subspace Clustering of High Dimensional Data for Data Mining Applications
Rakesh Agrawal, Johannes Gehrke, Dimitrios Gunopulos, Prabhakar Raghavan
Full Paper (PDF)

Abstract
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.

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BIBTEX

@inproceedings{DBLP:conf/sigmod/AgrawalGGR98,
author = {Rakesh Agrawal and
Johannes Gehrke and
Dimitrios Gunopulos and
Prabhakar Raghavan},
editor = {Laura M. Haas and
Ashutosh Tiwary},
title = {Automatic Subspace Clustering of High Dimensional Data for Data
Mining Applications},
booktitle = {SIGMOD 1998, Proceedings ACM SIGMOD International Conference
on Management of Data, June 2-4, 1998, Seattle, Washington, USA},
publisher = {ACM Press},
year = {1998},
isbn = {0-89791-955-5},
pages = {94-105},
crossref = {DBLP:conf/sigmod/98},
bibsource = {DBLP, http://dblp.uni-trier.de}
}


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