Automatic Subspace Clustering of High Dimensional Data

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Automatic Subspace Clustering of High Dimensional Data (2005)

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

Venue:	Data Mining and Knowledge Discovery
Citations:	723 - 12 self

BibTeX

@MISC{Agrawal05automaticsubspace,
    author = {Rakesh Agrawal and Johannes Gehrke and Dimitrios Gunopulos and Prabhakar Raghavan},
    title = {Automatic Subspace Clustering of High Dimensional Data},
    year = {2005}
}

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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.

Keyphrases

high dimensional data automatic subspace clustering input record data mining application canonical data distribution accurate cluster identical result specific mathematical form maximum dimensionality special requirement present clique large high dimensional datasets end-user comprehensibility dnf expression cluster description dense cluster data distribution

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