A Transaction-Based Neighbourhood-Driven Approach to Quantifying Interestingness of Association Rules

I
ICDM
2004
Fourth IEEE International Conference on Data Mining (ICDM'04)
Abstract - A Transaction-Based Neighbourhood-Driven Approach to Quantifying Interestingness of Association Rules

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Fourth IEEE International Conference on Data Mining (ICDM'04)

Brighton, United Kingdom

November 01-November 04

ISBN: 0-7695-2142-8

	ASCII Text	x
	B. Shekar, Rajesh Natarajan, "A Transaction-Based Neighbourhood-Driven Approach to Quantifying Interestingness of Association Rules," Data Mining, IEEE International Conference on, pp. 194-201, Fourth IEEE International Conference on Data Mining (ICDM'04), 2004.

	BibTex	x
	@article{ 10.1109/ICDM.2004.10107, author = {B. Shekar and Rajesh Natarajan}, title = {A Transaction-Based Neighbourhood-Driven Approach to Quantifying Interestingness of Association Rules}, journal ={Data Mining, IEEE International Conference on}, volume = {0}, year = {2004}, isbn = {0-7695-2142-8}, pages = {194-201}, doi = {http://doi.ieeecomputersociety.org/10.1109/ICDM.2004.10107}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Data Mining, IEEE International Conference on TI - A Transaction-Based Neighbourhood-Driven Approach to Quantifying Interestingness of Association Rules SN - 0-7695-2142-8 SP194 EP201 A1 - B. Shekar, A1 - Rajesh Natarajan, PY - 2004 KW - null VL - 0 JA - Data Mining, IEEE International Conference on ER -

B. Shekar, Indian Institute of Management Bangalore

Rajesh Natarajan, Indian Institute of Management Lucknow

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDM.2004.10107

In this paper, we present a data-driven approach for ranking association rules (ARs) based on interestingness. The occurrence of unrelated or weakly related item-pairs in an AR is interesting. In the retail market-basket context, items may be related through various relationships arising due to mutual interaction, 'substitutability' and 'complementarity.' Item-relatedness is a composite of these relationships. We introduce three relatedness measures for capturing relatedness between item-pairs. These measures use the concept of function embedding to appropriately weigh the relatedness contributions due to complementarity and substitutability between items. We propose an interestingness coefficient by combining the three relatedness measures. We compare this with two objective measures of interestingness and show the intuitiveness of the proposed interestingness coefficient.

Citation:

B. Shekar, Rajesh Natarajan, "A Transaction-Based Neighbourhood-Driven Approach to Quantifying Interestingness of Association Rules," icdm, pp.194-201, Fourth IEEE International Conference on Data Mining (ICDM'04), 2004

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