Towards generic pattern mining

Mohammed J. Zaki, Nagender Parimi, N. De, Feng Gao, Benjarath Phoophakdee, Joe Urban, Vineet Chaoji, Mohammad Al Hasan, Saeed Salem

Research output: Contribution to journalConference article

11 Citations (Scopus)

Abstract

Frequent Pattern Mining (FPM) is a very powerful paradigm for mining informative and useful patterns in massive, complex datasets. In this paper we propose the Data Mining Template Library, a collection of generic containers and algorithms for FPM, as well as persistency and database management classes. DMTL provides a systematic solution to a whole class of common FPM tasks like itemset, sequence, tree and graph mining. DMTL is extensible, scalable, and high-performance for rapid response on massive datasets. Our experiments show that DMTL is competitive with special purpose algorithms designed for a particular pattern type, especially as database sizes increase.

Original languageEnglish
Pages (from-to)1-20
Number of pages20
JournalLecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
Volume3403
Publication statusPublished - 24 Oct 2005
EventFormal Concept Analysis: Third International Conference, ICFCA 2005 - Lens, France
Duration: 14 Feb 200518 Feb 2005

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ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Zaki, M. J., Parimi, N., De, N., Gao, F., Phoophakdee, B., Urban, J., Chaoji, V., Al Hasan, M., & Salem, S. (2005). Towards generic pattern mining. Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 3403, 1-20.