SBKMMA: Sorting Based K Means and Median Based Clustering Algorithm Using Multi Machine Technique for Big Data

Authors

  • E. Mahima Jane Asst. Prof., Department of Computer Application , Madras Christian College, Tambaram – 600 059
  • Dr. E. George Dharma Prakash Raj Asst. Prof., Department of Computer Science and Engineering, Bharathidasan University, Trichy - 620 023

Keywords:

KMeans, BigData, Clustering, Sorting.

Abstract

Big data analytics examines large amounts of data to uncover hidden patterns, correlations and other insights. Clustering is the task of dividing the population or data points into a number of groups such that data points in the same groups are more similar to other data points in the same group than those in other groups. KMeans is a traditional partition algorithm which is simple and popularly used. This algorithm has disadvantages such as to identify K clusters, initial allocation etc. In this paper we mainly focus on the initial centroids and improving the efficiency by reducing the number of iterations. Sorting based KMeans algorithm and Sorting based KMedian algorithm are enhanced form of KMeans algorithm where the data are sorted and uses KMeans algorithm. The proposed algorithm focuses on the initial centroid selection with the help of sorting. Here the centroids are default assigned to the objects in the beginning after sorting. 

References

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Mahima Jane and Dr. E. George Dharma Prakash Raj “SBKMEDA : Sorting based K- Median Clustering Algorithm using Multi Machine Technique for Big Data “ accepted for Advances in Intelligent Systems and Computing.

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Published

2018-01-02

How to Cite

Jane, E. M., & Raj, D. E. G. D. P. (2018). SBKMMA: Sorting Based K Means and Median Based Clustering Algorithm Using Multi Machine Technique for Big Data. International Journal of Computer (IJC), 28(1), 1–7. Retrieved from https://ijcjournal.org/index.php/InternationalJournalOfComputer/article/view/1128

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