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Initial Centroid Selection Optimization for K-Means with Genetic Algorithm to Enhance Clustering of Transcribed Arabic Broadcast News Documents

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Applied Computational Intelligence and Mathematical Methods (CoMeSySo 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 662))

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Abstract

In this research a collection of artificial intelligence techniques are combined together to optimize the process of clustering textual transcripts obtained from audio sources. Since clustering techniques have drawbacks that if not taken care of will produce sub optimal clustering solutions, it’s essential to attempt to optimize the clustering algorithms to avoid sub optimal solutions. As an attempt to overcome this problem, different artificial intelligence techniques are applied to avoid clustering problems. The main objectives of this research is to optimize automatic topic clustering of transcribed speech documents, and investigate the impact of applying genetic algorithm optimization and initial centroid selection optimization (ICSO) in combination with K-means clustering algorithm using Chi-Square similarity measure on the accuracy and the sum of square distances (SSD) of the selected clustering algorithm. The evaluation showed that using ICSO with genetic algorithm and K-means clustering algorithm with Chi-square similarity measure achieved the highest accuracy with the least SSD.

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Author information

Authors and Affiliations

  1. Department of Computer Science, College of Computers and Information Systems, Arab Academy for Science and Technology (AAST), Cairo, Egypt

    Ahmed Mohamed Maghawry & Yasser Omar

  2. Department of Computer Science, Faculty of Computers and Information, Cairo University, Cairo, 12613, Egypt

    Amr Badr

Authors
  1. Ahmed Mohamed Maghawry
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  2. Yasser Omar
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  3. Amr Badr
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Correspondence to Ahmed Mohamed Maghawry .

Editor information

Editors and Affiliations

  1. Faculty of Applied Informatics, Tomas Bata University in Zlín, Zlín, Czech Republic

    Radek Silhavy

  2. Faculty of Applied Informatics, Tomas Bata University in Zlín, Zlín, Czech Republic

    Petr Silhavy

  3. Faculty of Applied Informatics, Tomas Bata University in Zlín, Zlín, Czech Republic

    Zdenka Prokopova

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Maghawry, A.M., Omar, Y., Badr, A. (2018). Initial Centroid Selection Optimization for K-Means with Genetic Algorithm to Enhance Clustering of Transcribed Arabic Broadcast News Documents. In: Silhavy, R., Silhavy, P., Prokopova, Z. (eds) Applied Computational Intelligence and Mathematical Methods. CoMeSySo 2017. Advances in Intelligent Systems and Computing, vol 662. Springer, Cham. https://doi.org/10.1007/978-3-319-67621-0_8

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  • DOI: https://doi.org/10.1007/978-3-319-67621-0_8

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  • Online ISBN: 978-3-319-67621-0

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