Frequent-pattern based iterative projected clustering

Man Lung Yiu; Nikos Mamoulis

Frequent-pattern based iterative projected clustering

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

53 Citations (Scopus)

Abstract

Irrelevant attributes add noise to high dimensional clusters and make traditional clustering techniques inappropriate. Projected clustering algorithms have been proposed to find the clusters in hidden subspaces. We realize the analogy between mining frequent itemsets and discovering the relevant subspace for a given cluster. We propose a methodology for finding projected clusters by mining frequent itemsets and present heuristics that improve its quality. Our techniques are evaluated with synthetic and real data; they are scalable and discover projected clusters accurately.

Original language	English
Title of host publication	Proceedings - 3rd IEEE International Conference on Data Mining, ICDM 2003
Pages	689-692
Number of pages	4
Publication status	Published - 1 Dec 2003
Externally published	Yes
Event	3rd IEEE International Conference on Data Mining, ICDM '03 - Melbourne, FL, United States Duration: 19 Nov 2003 → 22 Nov 2003

Conference

Conference	3rd IEEE International Conference on Data Mining, ICDM '03
Country/Territory	United States
City	Melbourne, FL
Period	19/11/03 → 22/11/03

ASJC Scopus subject areas

Engineering(all)

Cite this

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title = "Frequent-pattern based iterative projected clustering",

abstract = "Irrelevant attributes add noise to high dimensional clusters and make traditional clustering techniques inappropriate. Projected clustering algorithms have been proposed to find the clusters in hidden subspaces. We realize the analogy between mining frequent itemsets and discovering the relevant subspace for a given cluster. We propose a methodology for finding projected clusters by mining frequent itemsets and present heuristics that improve its quality. Our techniques are evaluated with synthetic and real data; they are scalable and discover projected clusters accurately.",

author = "Yiu, {Man Lung} and Nikos Mamoulis",

year = "2003",

month = dec,

day = "1",

language = "English",

isbn = "0769519784",

pages = "689--692",

booktitle = "Proceedings - 3rd IEEE International Conference on Data Mining, ICDM 2003",

note = "3rd IEEE International Conference on Data Mining, ICDM '03 ; Conference date: 19-11-2003 Through 22-11-2003",

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T1 - Frequent-pattern based iterative projected clustering

AU - Yiu, Man Lung

AU - Mamoulis, Nikos

PY - 2003/12/1

Y1 - 2003/12/1

N2 - Irrelevant attributes add noise to high dimensional clusters and make traditional clustering techniques inappropriate. Projected clustering algorithms have been proposed to find the clusters in hidden subspaces. We realize the analogy between mining frequent itemsets and discovering the relevant subspace for a given cluster. We propose a methodology for finding projected clusters by mining frequent itemsets and present heuristics that improve its quality. Our techniques are evaluated with synthetic and real data; they are scalable and discover projected clusters accurately.

AB - Irrelevant attributes add noise to high dimensional clusters and make traditional clustering techniques inappropriate. Projected clustering algorithms have been proposed to find the clusters in hidden subspaces. We realize the analogy between mining frequent itemsets and discovering the relevant subspace for a given cluster. We propose a methodology for finding projected clusters by mining frequent itemsets and present heuristics that improve its quality. Our techniques are evaluated with synthetic and real data; they are scalable and discover projected clusters accurately.

UR - http://www.scopus.com/inward/record.url?scp=20844440247&partnerID=8YFLogxK

M3 - Conference article published in proceeding or book

SN - 0769519784

SN - 9780769519784

SP - 689

EP - 692

BT - Proceedings - 3rd IEEE International Conference on Data Mining, ICDM 2003

T2 - 3rd IEEE International Conference on Data Mining, ICDM '03

Y2 - 19 November 2003 through 22 November 2003

ER -

Frequent-pattern based iterative projected clustering

Abstract

Conference

ASJC Scopus subject areas

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