Text Dimensionality Reduction for Document Clustering Using Hybrid Memetic Feature Selection

  • Ibraheem Al-Jadir
  • Kok Wai Wong
  • Chun Che Fung
  • Hong Xie
Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10607)

Abstract

In this paper, a document clustering method with a hybrid feature selection method is proposed. The proposed hybrid feature selection method integrates a Genetic-based wrapper method with ranking filter. The method is named Memetic Algorithm-Feature Selection (MA-FS). In this paper, MA-FS is combined with K-means and Spherical K-means (SK-means) clustering methods to perform document clustering. For the purpose of comparison, another unsupervised feature selection method, Feature Selection Genetic Text Clustering (FSGATC), is used. Two real-world criminal report document sets were used along with two popular benchmark datasets which are Reuters and 20newsgroup, were used in the comparisons. F-Micro, F-Macro and Average Distance of Document to Cluster (ADDC) measures were used for evaluation. The test results showed that the MA-FS method has outperformed the FSGATC method. It has also outperformed the results after using the entire feature space (ALL).

Keywords

Clustering Feature F-measure Hybrid Memetic Selection 
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Notes

Acknowledgment

Ibraheem wants to thank the Higher Committee for Education Development in Iraq (HCED) for the funning of his scholarship.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Ibraheem Al-Jadir
    • 1
    • 2
  • Kok Wai Wong
    • 1
  • Chun Che Fung
    • 1
  • Hong Xie
    • 1
  1. 1.School of Engineering and Information TechnologyMurdoch UniversityPerthAustralia
  2. 2.College of ScienceBaghdad UniversityBaghdadIraq

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