Volume-5 , Issue-1 , Dec 2018, ISSN 2348-635X Go Back

• Open Access   Article

  An Efficient Cluster Formation Approach for Handling Large Scale Data

  Anil, Rajendra Gupta

  Research Paper | Journal-Paper (WAJES)

  Vol.5 , Issue.1 , pp.1-6, Dec-2018

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  Abstract
  The basic K-means algorithm is the most widely used clustering algorithm and the best-known of the partitioning-based clustering methods. Choosing the initial centroids randomly results in poor clustering. Instead of optimal clustering (or global optimum), sub-optimal clustering (or local minimum) is obtained. One of the problems associated with the basic K-means algorithm is that empty clusters can be obtained if no points are allocated to a cluster during the assignment step. So implementation in basic k-mean algorithm gives better solution for large scale data analysis. In this paper, the k-mean algorithm and its implementation have done and the obtained results are also discussed. After the analysis, it is found that the implementation in k-mean algorithm provide better outcome.

  Key-Words / Index Term
  Basic k-mean, implemented k-mean, Large Scale Data, Fuzzy C4.5

  References
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  Citation
  Anil, Rajendra Gupta, "An Efficient Cluster Formation Approach for Handling Large Scale Data," World Academics Journal of Engineering Sciences, Vol.5, Issue.1, pp.1-6, 2018