Efficient Mining Top-k Regular-Frequent Itemset Using Compressed Tidsets

  • Komate Amphawan
  • Philippe Lenca
  • Athasit Surarerks
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7104)

Abstract

Association rule discovery based on support-confidence framework is an important task in data mining. However, the occurrence frequency (support) of a pattern (itemset) may not be a sufficient criterion for discovering interesting patterns. Temporal regularity, which can be a trace of behavior, with frequency behavior can be revealed as an important key in several applications. A pattern can be regarded as a regular pattern if it occurs regularly in a user-given period. In this paper, we consider the problem of mining top-k regular-frequent itemsets from transactional databases without support threshold. A new concise representation, called compressed transaction-ids set (compressed tidset), and a single pass algorithm, called TR-CT (Top-k Regular frequent itemset mining based on Compressed Tidsets), are proposed to maintain occurrence information of patterns and discover k regular itemsets with highest supports, respectively. Experimental results show that the use of the compressed tidset representation achieves highly efficiency in terms of execution time and memory consumption, especially on dense datasets.

Keywords

Association Rule Frequent Itemsets Mining Association Rule Memory Consumption Support Threshold 
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 2012

Authors and Affiliations

  • Komate Amphawan
    • 1
    • 2
    • 3
  • Philippe Lenca
    • 2
    • 3
  • Athasit Surarerks
    • 1
  1. 1.ELITE LaboratoryChulalongkorn UniversityBangkokThailand
  2. 2.Institut Telecom, Telecom BretagneUMR CNRS 3192 Lab-STICCFrance
  3. 3.Université européenne de BretagneFrance

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