A novel evolutionary method to search interesting association rules by keywords
In this paper, we propose an evolutionary method for directly mining interesting association rules. Most of the association rule mining methods give a large number of rules, and it is difficult for human beings to deal with them. We study this problem by borrowing the style of search engine, that is, searching association rules by keywords. Whether a rule is interesting or not is decided by its relation to the keywords, and we introduce both semantic and statistical methods to measure such relation. The mining process is built on an evolutionary approach, Genetic Network Programming (GNP). Different from the conventional GNP based association rule mining method, the proposed method pays more attention to generate the GNP individuals carefully, which will mine interesting association rules efficiently. After the rules are generated, they will be ranked and annotated by meaningful information, such as similar rules and representative transactions, in order to help the user to understand the rules better. We also discuss how to mine generalized interesting association rules, describing more abstract level of information than the common association rules. In the simulation section, we give some demonstrations of the proposed method using a census data set, which shows a promising way to find the interesting association rules. Crown Copyright (C) 2011 Published by Elsevier Ltd. All rights reserved.
association rule search engine genetic network programming semantic annotation