Mining unexpected rules by pushing user dynamics

Unexpected rules are interesting because they are either previously unknown or deviate from what prior user knowledge would suggest. In this paper, we study three important issues that have been previously ignored in mining unexpected rules. First, the unexpectedness of a rule depends on how the user prefers to apply the prior knowledge to a given scenario, in addition to the knowledge itself. Second, the prior knowledge should be considered right from the start to focus the search on unexpected rules. Third, the unexpectedness of a rule depends on what other rules the user has seen so far. Thus, only rules that remain unexpected given what the user has seen should be considered interesting. We develop an approach that addresses all three problems above and evaluate it by means of experiments focusing on finding interesting rules.

• Type: Conference
• Year: 2003
• Where: KDD
• Authors: Ke Wang, Yuelong Jiang, Laks V. S. Lakshmanan
• Keywords: Data Mining, KDD 2003, Mining Unexpected Rules, Unexpected Rules