Evolution and Maintenance of Frequent Pattern Space When Transactions Are Removed

  • Mengling Feng
  • Guozhu Dong
  • Jinyan Li
  • Yap-Peng Tan
  • Limsoon Wong
Conference paper

DOI: 10.1007/978-3-540-71701-0_50

Part of the Lecture Notes in Computer Science book series (LNCS, volume 4426)
Cite this paper as:
Feng M., Dong G., Li J., Tan YP., Wong L. (2007) Evolution and Maintenance of Frequent Pattern Space When Transactions Are Removed. In: Zhou ZH., Li H., Yang Q. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2007. Lecture Notes in Computer Science, vol 4426. Springer, Berlin, Heidelberg

Abstract

This paper addresses the maintenance of discovered frequent patterns when a batch of transactions are removed from the original dataset. We conduct an in-depth investigation on how the frequent pattern space evolves under transaction removal updates using the concept of equivalence classes. Inspired by the evolution analysis, an effective and exact algorithm TRUM is proposed to maintain frequent patterns. Experimental results demonstrate that our algorithm outperforms representative state-of-the-art algorithms.

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Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Mengling Feng
    • 1
  • Guozhu Dong
    • 2
  • Jinyan Li
    • 3
  • Yap-Peng Tan
    • 1
  • Limsoon Wong
    • 4
  1. 1.Nanyang Technological University 
  2. 2.Wright State University 
  3. 3.Institute for Infocomm Research 
  4. 4.National University of Singapore 

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