IceCube: efficient targeted mining in data cubes

Harsola, Shrutendra K and Deshpande, Prasad M and Haritsa, Jayant R (2012) IceCube: efficient targeted mining in data cubes. In: 2012 IEEE 12th International Conference on Data Mining (ICDM), 10-December 13, Brussels, Belgium, pp. 894-899.

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Abstract

We address the problem of mining targeted association rules over multidimensional market-basket data. Here, each transaction has, in addition to the set of purchased items, ancillary dimension attributes associated with it. Based on these dimensions, transactions can be visualized as distributed over cells of an n-dimensional cube. In this framework, a targeted association rule is of the form {X -> Y} R, where R is a convex region in the cube and X. Y is a traditional association rule within region R. We first describe the TOARM algorithm, based on classical techniques, for identifying targeted association rules. Then, we discuss the concepts of bottom-up aggregation and cubing, leading to the CellUnion technique. This approach is further extended, using notions of cube-count interleaving and credit-based pruning, to derive the IceCube algorithm. Our experiments demonstrate that IceCube consistently provides the best execution time performance, especially for large and complex data cubes.

Item Type:	Conference Paper
Series.:	IEEE International Conference on Data Mining
Publisher:	IEEE
Additional Information:	Copyright of this article belongs to IEEE.
Keywords:	Data cube; Association rule Mining; Localized Rules
Department/Centre:	Division of Electrical Sciences > Computer Science & Automation
Date Deposited:	04 Jun 2013 12:00
Last Modified:	04 Jun 2013 12:00
URI:	http://eprints.iisc.ac.in/id/eprint/46532

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