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Cited by 663

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Automatic subspace clustering of high dimensional data for data mining applications

Open access
Agrawal, Rakesh, Gehrke, Johannes, Gunopulos, Dimitrios, Raghavan, Prabhakar·1998·ACM SIGMOD Record
DOI 10.1145/276305.276314
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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 cluster in large high dimensional datasets.

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Metadata

Authors
Agrawal, R., Gehrke, J., Gunopulos, D., & Raghavan, P.
Venue
ACM SIGMOD Record
Publisher
Association for Computing Machinery (ACM)
Published
1998-06
Volume
27
Issue
2
Pages
94-105
ISSN
0163-5808 (ISSN-L)
DOI
10.1145/276305.276314
OpenAlex
W3003734944
Cited by
663
References
1
FWCI
2.63
Type
journal article
Language
en
Open access
bronze
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)

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CoChat. (2026). Citation check: Automatic subspace clustering of high dimensional data for data mining applications. https://app.cochat.ai/papers/10.1145/276305.276314
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Agrawal, R., Gehrke, J., Gunopulos, D., & Raghavan, P. (1998). Automatic subspace clustering of high dimensional data for data mining applications. ACM SIGMOD Record. https://doi.org/10.1145/276305.276314

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