Abstract
Episode rule mining is a popular data mining task for analyzing a sequence of events or symbols. It consists of identifying subsequences of events that frequently appear in a sequence and then to combine them to obtain episode rules that reveal strong relationships between events. But a key problem is that each rule requires a strict ordering of events. As a result, similar rules are treated differently, though they in practice often describe a same situation. To find a smaller set of rules that are more general and can replace numerous episode rules, this paper introduces a novel type of rules called partially-ordered episode rules, where events in a rule are partially ordered. To efficiently find all these rules in a sequence, an efficient algorithm named POERM (Partially-Ordered Episode Rule Miner) is presented. An experimental evaluation on several benchmark dataset shows that POERM has excellent performance.
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