Abstract
Mining association rules is an important task for knowledge discovery. We can analyze past transaction data to discover customer behaviors such that the quality of business decisions can be improved. Various types of association rules may exist in a large database of customer transactions. The strategy of mining association rules focuses on discovering large item sets, which are groups of items which appear together in a sufficient number of transactions. We propose a graph-based approach to generate various types of association rules from a large database of customer transactions. This approach scans the database once to construct an association graph and then traverses the graph to generate all large item sets. Empirical evaluations show that our algorithms outperform other algorithms which need to make multiple passes over the database.
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