Frequent-Pattern based Iterative Projected Clustering

I
ICDM
2003
Third IEEE International Conference on Data Mining (ICDM'03)
Abstract - Frequent-Pattern based Iterative Projected Clustering

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	Similar Articles Articles by Man Lung Yiu Articles by Nikos Mamoulis

Third IEEE International Conference on Data Mining (ICDM'03)

Melbourne, Florida

November 19-November 22

ISBN: 0-7695-1978-4

	ASCII Text	x
	Man Lung Yiu, Nikos Mamoulis, "Frequent-Pattern based Iterative Projected Clustering," Data Mining, IEEE International Conference on, pp. 689, Third IEEE International Conference on Data Mining (ICDM'03), 2003.

	BibTex	x
	@article{ 10.1109/ICDM.2003.1251009, author = {Man Lung Yiu and Nikos Mamoulis}, title = {Frequent-Pattern based Iterative Projected Clustering}, journal ={Data Mining, IEEE International Conference on}, volume = {0}, year = {2003}, isbn = {0-7695-1978-4}, pages = {689}, doi = {http://doi.ieeecomputersociety.org/10.1109/ICDM.2003.1251009}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Data Mining, IEEE International Conference on TI - Frequent-Pattern based Iterative Projected Clustering SN - 0-7695-1978-4 SP EP A1 - Man Lung Yiu, A1 - Nikos Mamoulis, PY - 2003 KW - null VL - 0 JA - Data Mining, IEEE International Conference on ER -

Man Lung Yiu, University of Hong Kong, Pokfulam Road

Nikos Mamoulis, University of Hong Kong, Pokfulam Road

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDM.2003.1251009

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.

Citation:

Man Lung Yiu, Nikos Mamoulis, "Frequent-Pattern based Iterative Projected Clustering," icdm, pp.689, Third IEEE International Conference on Data Mining (ICDM'03), 2003

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