A transaction-based neighbourhood-driven approach to quantifying interestingness of association rules

Description

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. © 2004 IEEE.

Publication Date

1-1-2004

DOI

10.1109/ICDM.2004.10107

ISBN

0769521428||978-0769521428

Publisher

IEEE

Keywords

Association rules, Technology management, Data mining, Management information systems, Information technology, Manufacturing, Industrial relations, data mining, Transaction processing, Transaction-based neighbourhood-driven approach, Association rules, Retail market-basket, Item relatedness, Item pairs, Junction embedding, Relatedness contribution, Interestingness coefficient

Conference

Fourth IEEE International Conference on Data Mining, ICDM 2004: 1-4 November, 2004, Brighton, United Kingdom

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