Discovering Interesting Rules From Dense Data

摘要

Discovering association rules is one of the most important tasks in data mining and many efficient algorithms have been proposed in literature. However, the number of discovered rules is often so large, especially in dense data, that the user cannot analyze all discovered rules. To overcome that problem several methods for mining only interesting rules have been proposed. In this paper we describe efficient algorithm for finding maximal, unknown part of association with a given antecedent or consequent in databases with long patterns.