Automatic Subspace Clustering of High Dimensional Data

Automatic Subspace Clustering of High Dimensional Data (2005)

by Rakesh Agrawal , Johannes Gehrke , Dimitrios Gunopulos , Prabhakar Raghavan

Venue:	Data Mining and Knowledge Discovery
Citations:	461 - 11 self

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Datum	Value	Source
TITLE	Automatic Subspace Clustering of High Dimensional Data	SVM HeaderParse 0.2
AUTHOR NAME	Rakesh Agrawal	user correction
AUTHOR NAME	Johannes Gehrke	user correction
AUTHOR NAME	Dimitrios Gunopulos	user correction
AUTHOR NAME	Prabhakar Raghavan	user correction
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.	user correction
YEAR	2005	user correction
VENUE	Data Mining and Knowledge Discovery	user correction
PAGES	5--33	user correction
VOLUME	11	user correction
NUMBER	1	user correction
CITATIONS	52 found	ParsCit 1.0