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

E. Mahima Jane, Dr. E. George Dharma Prakash Raj

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. 


Keywords


KMeans;BigData; Clustering; Sorting.

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References


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