Mining Actionable Partial Orders in Collections of Sequences

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.

[1]  Jianyong Wang,et al.  Mining sequential patterns by pattern-growth: the PrefixSpan approach , 2004, IEEE Transactions on Knowledge and Data Engineering.

[2]  Xiangji Huang,et al.  Comparison of interestingness functions for learning web usage patterns , 2002, CIKM '02.

[3]  Mikhail J. Atallah,et al.  Reliable detection of episodes in event sequences , 2004, Knowledge and Information Systems.

[4]  Ramakrishnan Srikant,et al.  Mining sequential patterns , 1995, Proceedings of the Eleventh International Conference on Data Engineering.

[5]  Philip S. Yu,et al.  Discovering Frequent Closed Partial Orders from Strings , 2006, IEEE Transactions on Knowledge and Data Engineering.

[6]  Mikhail J. Atallah,et al.  Markov Models for Identification of Significant Episodes , 2005, SDM.

[7]  Doron Rotem,et al.  An Algorithm to Generate all Topological Sorting Arrangements , 1981, Computer/law journal.

[8]  Mikhail J. Atallah,et al.  Detection of significant sets of episodes in event sequences , 2004, Fourth IEEE International Conference on Data Mining (ICDM'04).

[9]  Jayme Luiz Szwarcfiter,et al.  A Structured Program to Generate all Topological Sorting Arrangements , 1974, Information Processing Letters.

[10]  Frank Ruskey,et al.  Generating Linear Extensions Fast , 1994, SIAM J. Comput..

[11]  Xifeng Yan,et al.  CloSpan: Mining Closed Sequential Patterns in Large Datasets , 2003, SDM.

[12]  Gemma C. Garriga,et al.  Summarizing Sequential Data with Closed Partial Orders , 2005, SDM.

[13]  Zhe Wang,et al.  Mining Maximal Sequential Patterns , 2005, 2005 International Conference on Neural Networks and Brain.

[14]  Heikki Mannila,et al.  Global partial orders from sequential data , 2000, KDD '00.

[15]  Jiawei Han,et al.  Frequent pattern mining: current status and future directions , 2007, Data Mining and Knowledge Discovery.

[16]  Fabio Crestani,et al.  Ranking Sequential Patterns with Respect to Significance , 2010, PAKDD.