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Feature Selection and Enhanced Krill Herd Algorithm for Text Document Clustering pp 61–103Cite as

Proposed Methodology

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 816))

Abstract

This chapter presents and summarizes the proposed method that was used to achieve the research objectives of the present study, including (i) a new weight scheme, that is, length feature weight (LFW) in Sect. 4.4.1; (ii) three models for TFSP to find the best algorithm for the FS problem in Sect. 4.5; (iii) a dynamic DR technique in Sect. 4.6; (iv) three models of the KHA for TDCP in Sect. 4.8; (v) a new multi-objective function for enhancing the clustering decision of the local search algorithm in Sect. 4.8; (vi) experiments and results in Sect. 4.9; and (vii) conclusion in Sect. 4.10.

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Notes

  1. 1.

    http://www.unine.ch/Info/clef/.

  2. 2.

    Porter stemmer. Website at http://tartarus.org/martin/PorterStemmer/.

  3. 3.

    http://text-processing.com/demo/.

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Authors and Affiliations

  1. Universiti Sains Malaysia, Penang, Malaysia

    Laith Mohammad Qasim Abualigah

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  1. Laith Mohammad Qasim Abualigah
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Correspondence to Laith Mohammad Qasim Abualigah .

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Abualigah, L.M.Q. (2019). Proposed Methodology. In: Feature Selection and Enhanced Krill Herd Algorithm for Text Document Clustering. Studies in Computational Intelligence, vol 816. Springer, Cham. https://doi.org/10.1007/978-3-030-10674-4_4

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