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Erschienen in:
2011 | OriginalPaper | Buchkapitel
Mining Actionable Partial Orders in Collections of Sequences
verfasst von : Robert Gwadera, Gianluca Antonini, Abderrahim Labbi
Erschienen in: Machine Learning and Knowledge Discovery in Databases
Verlag: Springer Berlin Heidelberg
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Mining frequent partial orders from a collection of sequences was introduced as an alternative to mining frequent sequential patterns in order to provide a more compact/understandable representation. The motivation was that a single partial order can represent the same ordering information between items in the collection as a set of sequential patterns (set of totally ordered sets of items). However, in practice, a discovered set of frequent partial orders is still too large for an effective usage. We address this problem by proposing a method for ranking partial orders with respect to significance that extends our previous work on ranking sequential patterns. In experiments, conducted on a collection of visits to a website of a multinational technology and consulting firm we show the applicability of our framework to discover partial orders of frequently visited webpages that can be actionable in optimizing effectiveness of web-based marketing.