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SBKMEDA: Sorting-Based K-Median Clustering Algorithm Using Multi-Machine Technique for Big Data

  • E. Mahima Jane
  • E. George Dharma Prakash Raj
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 645)

Abstract

Big Data is the term that describes any voluminous amount of structured, semi-structured and unstructured data that has the potential to be mined for information. Clustering is an essential tool for clustering Big Data. Multi-machine clustering technique is one of the very efficient methods used in the Big Data to mine and analyse the data for insights. K-Means partition-based clustering algorithm is one of the clustering algorithm used to cluster Big Data. One of the main disadvantage of K-Means clustering algorithms is the deficiency in randomly identifying the K number of clusters and centroids. This results in more number of iterations and increased execution times to arrive at the optimal centroid. Sorting-based K-Means clustering algorithm (SBKMA) using multi-machine technique is another method for analysing Big Data. In this method, the data is sorted first using Hadoop MapReduce and mean is taken as centroids. This paper proposes a new algorithm called as SBKMEDA: Sorting-based K-Median clustering algorithm using multi-machine technique for Big Data to sort the data and replace median with mean as centroid for better accuracy and speed in forming the cluster.

Keywords

Big Data Clustering K-Means algorithm Hadoop MapReduce SBKMA 

References

  1. 1.
    Jane, M., George Dharma Prakash Raj, E.: SBKMA: sorting based K-Means clustering algorithm using multi machine technique for Big Data. Int. J. Control Theory Appl. 8, 2105–2110 (2015)Google Scholar
  2. 2.
    Vrinda, Patil, S.: Efficient clustering of data using improved K-Means algorithm—a review. Imp. J. Interdiscip. Res. 2(1) (2016)Google Scholar
  3. 3.
    Patil, Y.S., Vaidya, M.B.: K-Means clustering with MapReduce technique. Int. J. Adv. Res. Comput. Commun. Eng. (2015)Google Scholar
  4. 4.
    Baswade, A.M., Nalwade, P.S.: Selection of initial centroids for K-Means Algorithm. IJCSMC 2(7), 161–164 (2013)Google Scholar
  5. 5.
    Vishnupriya, N., Sagayaraj Francis, F.: Data clustering using MapReduce for multidimensional datasets. Int. Adv. Res. J. Sci. Eng. Technol. (2015)Google Scholar
  6. 6.
    Gandhi, G., Srivastava, R.: Review paper: a comparative study on partitioning techniques of clustering algorithms. Int. J. Comput. Appl. (0975-8887) 87(9) (2014)Google Scholar
  7. 7.
    Bobade, V.B.: Survey paper on Big Data and Hadoop. Int. Res. J. Eng. Technol. (IRJET) 03(01) (2016)Google Scholar
  8. 8.
    Rauf, A., Sheeba, Mahfooz, S., Khusro, S., Javed, H.: Enhanced K-Mean clustering algorithm to reduce number of iterations and time complexity. Middle-East J. Sci. Res. 12(7), 959–963 (2012)Google Scholar
  9. 9.

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of Computer ApplicationMadras Christian CollegeTambaramIndia
  2. 2.Department of Computer Science and EngineeringBharathidasan UniversityTrichyIndia

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