Implementation of Parallelized K-Means and K-Medoids++ Clustering Algorithms on Hadoop Map Reduce Framework
Maithri. C1, Chandramouli. H2

1Maithri. C, Associate Professor, Department of CSE, Kalpataru Institute of Technology, Tiptur (Karnataka), India.

2Dr. Chandramouli. H, Professor, Department of CSE, East Point College of Engineering and Technology Bangalore (Karnataka), India.

Manuscript received on 06 December 2019 | Revised Manuscript received on 14 December 2019 | Manuscript Published on 31 December 2019 | PP: 530-535 | Volume-9 Issue-2S December 2019 | Retrieval Number: B10451292S19/2019©BEIESP | DOI: 10.35940/ijitee.B1045.1292S19

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Abstract: The electronic information from online newspapers, journals, conference proceedings website pages and emails are growing rapidly which are generating huge amount of data. Data grouping has been gotten impressive consideration in numerous applications. The size of data is raised exponentially due to the advancement of innovation and development, makes clustering of vast size of information, a challenging issue. With the end goal to manage the issue, numerous scientists endeavor to outline productive parallel clustering representations to be needed in algorithms of hadoop. In this paper, we show the implementation of parallelized K-Means and parallelized K-Medoids algorithms for clustering an large data objects file based on MapReduce for grouping huge information. The proposed algorithms combines initialization algorithm with Map Reduce framework to reduce the number of iterations and it can scale well with the commodity hardware as the efficient process for large dataset processing. The outcome of this paper shows the implementation of each algorithms.

Keywords: Big Data, Clustering Algorithms, Hadoop, K-means, K-Medoids, K-Medoids++, MapReduce.
Scope of the Article: Clustering