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Review of Computer Engineering Research

June 2017, Volume 4, 2, pp 54-80

Comprehensive Analysis & Performance Comparison of Clustering Algorithms for Big Data

Anand Nayyar

,

Vikram Puri

Anand Nayyar 1

Vikram Puri 2

  1. Assistant Professor, Department of Computer Applications & IT, KCL Institute of Management and Technology, Jalandhar 1

  2. Research Scholar, Department of Electronics and Communications Engineering (ECE) Guru Nanak Dev University (Regional Campus), Jalandhar 2

on Google Scholar
on PubMed

Pages: 54-80

DOI: 10.18488/journal.76.2017.42.54.80

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Article History:

Received: 29 May, 2017
Revised: 06 October, 2017
Accepted: 12 October, 2017
Published: 17 October, 2017


Abstract:

21st Century has marked high velocity of data generation not only in terms of size but also in variety. Analyzing large data sets with different forms is also a challenging task. Data Mining is regarded as efficient method to extract meaningful information as per user requirements. But considering the size of modern data, traditional data mining techniques are failing. Clustering can be regarded as one of the most important technique to mine the data by splitting large data sets into clusters. The paper’s primary contribution is to provide comprehensive analysis of Big Data Clustering algorithms on basis of: Partitioning, Hierarchical, Density, Grid and Model. In addition to this, performance comparison of algorithms is performed on basis of volume, variety and velocity.
Contribution/ Originality

Keywords:

Big data, Clustering, Clustering algorithms, Partitioning-based clustering, Grid-Based clustering, Hierarchical clustering, Density-based clustering, Model based clustering, Data mining.

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Funding:

This study received no specific financial support.

Competing Interests:

The authors declare that they have no competing interests.

Acknowledgement:

Both authors contributed equally to the conception and design of the study.

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