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An Efficient Grouping Genetic Algorithm for Data Clustering and Big Data Analysis

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Computational Intelligence for Big Data Analysis

Part of the book series: Adaptation, Learning, and Optimization ((ALO,volume 19))

Abstract

Clustering as a formal, systematic subject in dissertations can be considered the most influential unsupervised learning problem; so, as every other problem of this kind, it deals with finding the structure in a collection of unlabeled data. One of the matters associated with this subject is undoubtedly determination of the number of clusters. In this chapter, an efficient grouping genetic algorithm is proposed under the circumstances of an anonymous number of clusters. Concurrent clustering with different number of clusters is implemented on the same data in each chromosome of grouping genetic algorithm in order to discern the accurate number of clusters. In subsequent iterations of the algorithm, new solutions with different clusters number or distinct accuracy of clustering are produced by application of efficient crossover and mutation operators that led to significant improvement of clustering. Furthermore, a local search by a special probability is applied in each chromosome of each new population in order to increase the accuracy of clustering.These special operators will lead to the successful application of the proposed method in the big data analysis. To prove the accuracy and the efficiency of the algorithm, its tested on various artificial and real data sets in a comparable manner. Most of the datasets consisted of overlapping clusters, but the algorithm could detect the proper number of all data sets with high accuracy of clustering. The consequences make the best evidence of the algorithms successful performance of finding an appropriate number of clusters and accomplishment of the best clusterings quality in comparison with others.

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Author information

Authors and Affiliations

  1. Department of Knowledge Engineering and Decision Science, University of Economic Science, Tehran, Iran

    Sayede Houri Razavi

  2. Department of Mathematics and Computer Science, University of Economic Science, Tehran, Iran

    E. Omid Mahdi Ebadati

  3. Department of Industrial Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran

    Shahrokh Asadi

  4. Department of Computer Science, Hamdard University, New Delhi, India

    Harleen Kaur

Authors
  1. Sayede Houri Razavi
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  2. E. Omid Mahdi Ebadati
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  3. Shahrokh Asadi
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  4. Harleen Kaur
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Corresponding author

Correspondence to Sayede Houri Razavi .

Editor information

Editors and Affiliations

  1. School of Computing Sciences and Engineering, VIT University, Vellore, India

    D.P. Acharjya

  2. Department of Information and Communication Technology, Fakir Mohan University, Balasore, India

    Satchidananda Dehuri

  3. Corporate Technology Office, Tata Consultancy Services, Mumbai, Maharashtra, India

    Sugata Sanyal

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Razavi, S.H., Ebadati, E.O.M., Asadi, S., Kaur, H. (2015). An Efficient Grouping Genetic Algorithm for Data Clustering and Big Data Analysis. In: Acharjya, D., Dehuri, S., Sanyal, S. (eds) Computational Intelligence for Big Data Analysis. Adaptation, Learning, and Optimization, vol 19. Springer, Cham. https://doi.org/10.1007/978-3-319-16598-1_5

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  • DOI: https://doi.org/10.1007/978-3-319-16598-1_5

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16597-4

  • Online ISBN: 978-3-319-16598-1

  • eBook Packages: EngineeringEngineering (R0)

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