CRoM and HuspExt: Improving Efficiency of High Utility Sequential Pattern Extraction


Alkan O. K. , KARAGÖZ P.

32nd IEEE International Conference on Data Engineering (ICDE), Helsinki, Finland, 16 - 20 May 2016, pp.1472-1473 identifier

  • Publication Type: Conference Paper / Full Text
  • City: Helsinki
  • Country: Finland
  • Page Numbers: pp.1472-1473

Abstract

This paper presents efficient data structures and a pruning technique in order to improve the efficiency of high utility sequential pattern mining. CRoM (Cumulated Rest of Match) based upper bound, which is a tight upper bound on the utility of the candidates is proposed in order to perform more conservative pruning before candidate pattern generation in comparison to the existing techniques. In addition, an efficient algorithm, HuspExt (High Utility Sequential Pattern Extraction), is presented which calculates the utilities of the child patterns based on that of the parents'. Substantial experiments on both synthetic and real datasets from different domains show that, the solution efficiently discovers high utility sequential patterns under low thresholds.