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Frequent Pattern Mining Guided Tabu Search

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Data Science Analytics and Applications (DaSAA 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 804))

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Abstract

The paper focuses on the search perspective of Data Mining. Genetic Algorithms, Tabu Search are a few of the evolutionary search strategies in the literature. As a part of this work, we relate Data mining in the context of Tabu Search to perform search for global optimum in a guided fashion. Sequence Pattern Mining (SPM) in generic and a variant of it, namely Maximal Sequence Pattern (MSP) is incorporated as a part of Tabu Search. The limitation of conventional Tabu Search is that the convergence rate is dependent on the initial population. The project proposes to arrive at an initial population employing SPM and hence result in improved convergence, in relation to conventional Tabu Search. The proposed algorithm has been tested for the N-Queens problem and empirical results indicate approximately 21% improved convergence.

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Authors and Affiliations

  1. Department of Computer Engineering, Indian Institute of Information Technology, Design and Manufacturing Kancheepuram, Chennai, India

    Sandeep Avula, C. Oswald & B. Sivaselvan

Authors
  1. Sandeep Avula
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  2. C. Oswald
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  3. B. Sivaselvan
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Corresponding author

Correspondence to C. Oswald .

Editor information

Editors and Affiliations

  1. Crescent University, Chennai, India

    Shriram R

  2. Birmingham City University, Birmingham, United Kingdom

    Mak Sharma

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Avula, S., Oswald, C., Sivaselvan, B. (2018). Frequent Pattern Mining Guided Tabu Search. In: R, S., Sharma, M. (eds) Data Science Analytics and Applications. DaSAA 2017. Communications in Computer and Information Science, vol 804. Springer, Singapore. https://doi.org/10.1007/978-981-10-8603-8_6

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  • DOI: https://doi.org/10.1007/978-981-10-8603-8_6

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-8602-1

  • Online ISBN: 978-981-10-8603-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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