Generalized Association Rule Mining Using Genetic Algorithms
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Date
2008
Journal Title
Journal ISSN
Volume Title
Publisher
Fountain Publishers
Abstract
We formulate a general Association rule mining model for extracting useful
information from very large databases. An interactive Association rule mining
system is designed using a combination of genetic algorithms and a modified a-priori
based algorithm. The association rule mining problem is modeled as a multi-objective
combinatorial problem which is solved using genetic algorithms. The combination
of genetic algorithms with a-priori query optimization make association rule mining
yield fast results. In this paper we use the same combination to extend it to a much
more general context allowing efficient mining of very large databases for many
different kinds of patterns. Given a large database of transactions, where each
transaction consists of a set of items, and a taxonomy (is-a hierarchy) on the items,
we find associations between items at any level of the taxonomy. We show how the
idea can be used either in a general purpose mining system or in a next generation of
conventional query optimizers.
Description
Keywords
Rule Mining, Genetic Algorithms
Citation
Wakabi-Waiswa, P. P., Baryamureeba, V., & Sarukesi, K. (2008). Generalized Association Rule Mining Using Genetic Algorithms. Strengthening the Role of ICT in Development, 59. ISBN 978-9970-02-871-2