Mining Quantitative Frequent Itemsets Using Adaptive Density-Based Subspace Clustering

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Fifth IEEE International Conference on Data Mining (ICDM'05)

Houston, Texas

November 27-November 30

ISBN: 0-7695-2278-5

	ASCII Text	x
	Takashi Washio, Yuki Mitsunaga, Hiroshi Motoda, "Mining Quantitative Frequent Itemsets Using Adaptive Density-Based Subspace Clustering," Data Mining, IEEE International Conference on, pp. 793-796, Fifth IEEE International Conference on Data Mining (ICDM'05), 2005.

	BibTex	x
	@article{ 10.1109/ICDM.2005.100, author = {Takashi Washio and Yuki Mitsunaga and Hiroshi Motoda}, title = {Mining Quantitative Frequent Itemsets Using Adaptive Density-Based Subspace Clustering}, journal ={Data Mining, IEEE International Conference on}, volume = {0}, year = {2005}, issn = {1550-4786}, pages = {793-796}, doi = {http://doi.ieeecomputersociety.org/10.1109/ICDM.2005.100}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Data Mining, IEEE International Conference on TI - Mining Quantitative Frequent Itemsets Using Adaptive Density-Based Subspace Clustering SN - 1550-4786 SP793 EP796 A1 - Takashi Washio, A1 - Yuki Mitsunaga, A1 - Hiroshi Motoda, PY - 2005 KW - null VL - 0 JA - Data Mining, IEEE International Conference on ER -

Takashi Washio, Osaka University

Yuki Mitsunaga, Osaka University

Hiroshi Motoda, Osaka University

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDM.2005.100

A novel approach to subspace clustering is proposed to exhaustively and efficiently mine quantitative frequent itemsets (QFIs) from massive transaction data¹. For the computational tractability, our approach introduces adaptive density-based and Apriori-like algorithm. Its outstanding performance is shown through numerical experiments.

Citation:

Takashi Washio, Yuki Mitsunaga, Hiroshi Motoda, "Mining Quantitative Frequent Itemsets Using Adaptive Density-Based Subspace Clustering," icdm, pp.793-796, Fifth IEEE International Conference on Data Mining (ICDM'05), 2005

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