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A Hybrid Clustering Technique to Improve Big Data Accessibility Based on Machine Learning Approaches

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Information Systems Design and Intelligent Applications

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 433))

Abstract

Big data is called to a large or complex data from traditional ones, which is unstructured in many case. Accessing to a specific value in a huge data that is not sorted or organized can be time consuming and require a high processing. With growing of data, clustering can be a most important unsupervised approach that finds a structure for data. In this paper, we demonstrate two approaches to cluster data with high accuracy, and then we sort data by implementing merge sort algorithm finally, we use binary search to find a data value point in a specific range of data. This research presents a high value efficiency combo method in big data by using genetic and k-means. After clustering with k-means total sum of the Euclidean distances is 3.37233e+09 for 4 clusters, and after genetic algorithm this number reduce to 0.0300344 in the best fit. In the second and third stage we show that after this implementation, we can access to a particular data much faster and accurate than other older methods.

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Author information

Authors and Affiliations

  1. Department of Mathematics and Computer Science, Kharazmi University, #242, Somayeh Street, Between Qarani & Vila, Tehran, Iran

    E. Omid Mahdi Ebadati

  2. Department of Knowledge Engineering and Decision Science, Kharazmi University, #242, Somayeh Street, Between Qarani & Vila, Tehran, Iran

    Mohammad Mortazavi Tabrizi

Authors
  1. E. Omid Mahdi Ebadati
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  2. Mohammad Mortazavi Tabrizi
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Corresponding author

Correspondence to E. Omid Mahdi Ebadati .

Editor information

Editors and Affiliations

  1. Deparment of CSE, Anil Neerukonda Ins. of Tech. & Sci., Vishakapatnam, India

    Suresh Chandra Satapathy

  2. Kalyani University, Nadia, West Bengal, India

    Jyotsna Kumar Mandal

  3. University of Hyderabad, Hyderabad, Andhra Pradesh, India

    Siba K. Udgata

  4. Dept. of ECE, Shri Ramswaroop Mem. Group of Prof. Clg, Lucknow, Uttar Pradesh, India

    Vikrant Bhateja

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Ebadati, E.O.M., Tabrizi, M.M. (2016). A Hybrid Clustering Technique to Improve Big Data Accessibility Based on Machine Learning Approaches. In: Satapathy, S., Mandal, J., Udgata, S., Bhateja, V. (eds) Information Systems Design and Intelligent Applications. Advances in Intelligent Systems and Computing, vol 433. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2755-7_43

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  • DOI: https://doi.org/10.1007/978-81-322-2755-7_43

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  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-322-2753-3

  • Online ISBN: 978-81-322-2755-7

  • eBook Packages: EngineeringEngineering (R0)

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