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Mining Actionable Partial Orders in Collections of Sequences

  • Robert Gwadera
  • Gianluca Antonini
  • Abderrahim Labbi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6911)

Abstract

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.

Keywords

Partial Order Sequential Pattern Linear Extension Parallel Pattern Serial Pattern 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Robert Gwadera
    • 1
  • Gianluca Antonini
    • 1
  • Abderrahim Labbi
    • 1
  1. 1.IBM Zurich Research LaboratoryRüschlikonSwitzerland

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