AKM—Augmentation of K-Means Clustering Algorithm for Big Data

  • Puja Shrivastava
  • Laxman Sahoo
  • Manjusha Pandey
  • Sandeep Agrawal
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 695)

Abstract

Clustering for big data analytics is a growing subject due to the large size of variety data sets needed to be analyzed in distributed and parallel environment. An augmentation of K-Means clustering algorithm is projected and evaluated here for MapReduce framework by using the concepts of genetic algorithm steps. Chromosome formation, fitness calculation, optimization, and crossover logics are used to overcome the problem of suboptimal solutions of K-Means clustering algorithm and reduction of time complexity of genetic K-Means algorithm for big data. Proposed algorithm is not dealing with the selection of parents to be sent to mating pool and mutation steps, so the performance time is improved.

Keywords

Big data analytics K-Means Genetic clustering Chromosome Optimized cluster 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Puja Shrivastava
    • 1
  • Laxman Sahoo
    • 1
  • Manjusha Pandey
    • 1
  • Sandeep Agrawal
    • 1
  1. 1.School of Computer EngineeringKIIT UniversityBhubaneswarIndia

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