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Performance Analysis of Parallel K-Means with Optimization Algorithms for Clustering on Spark

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10722)

Abstract

Clustering divides data into meaningful, useful groups known as clusters without any prior knowledge about the data. One of the drawbacks of K-Means clustering is the estimation of initial centroids which influence the performance of the algorithm. To overcome this issue, optimization algorithms like Bat and Firefly are executed as pre-processing step. These algorithms return optimal centroids which is given as input to the K-Means algorithm. Clustering is carried out on large data sets, therefore Apache Spark, an open source software framework is used. The performance of the optimization algorithms is evaluated and the best algorithm is determined.

Keywords

Clustering K-Means Bat algorithm Firefly algorithm Big data Spark 

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.PSG College of TechnologyAnna UniversityCoimbatoreIndia

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