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Big data analytics and data mining are techniques used to analyze data and to extract hidden information. Traditional approaches to analysis and extraction do not work well for big data because this data is complex and of very high volume. A major data mining technique known as data clustering groups the data into clusters and makes it easy to extract information from these clusters. However, existing clustering algorithms, such as k-means and hierarchical, are not efficient as the quality of the clusters they produce is compromised. Therefore, there is a need to design an efficient and highly scalable clustering algorithm. In this paper, we put forward a new clustering algorithm called hybrid clustering in order to overcome the disadvantages of existing clustering algorithms. We compare the new hybrid algorithm with existing algorithms on the bases of precision, recall, F-measure, execution time, and accuracy of results. From the experimental results, it is clear that the proposed hybrid clustering algorithm is more accurate, and has better precision, recall, and F-measure values.


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A Novel Clustering Technique for Efficient Clustering of Big Data in Hadoop Ecosystem

Show Author's information Sunil Kumar( )Maninder Singh
Directorate of Livestock Farms, Guru Angad Dev Veterinary and Animal Sciences University, Ludhiana 141001, India.
Department of Computer Science, Punjabi University, Punjab 147002, India.

Abstract

Big data analytics and data mining are techniques used to analyze data and to extract hidden information. Traditional approaches to analysis and extraction do not work well for big data because this data is complex and of very high volume. A major data mining technique known as data clustering groups the data into clusters and makes it easy to extract information from these clusters. However, existing clustering algorithms, such as k-means and hierarchical, are not efficient as the quality of the clusters they produce is compromised. Therefore, there is a need to design an efficient and highly scalable clustering algorithm. In this paper, we put forward a new clustering algorithm called hybrid clustering in order to overcome the disadvantages of existing clustering algorithms. We compare the new hybrid algorithm with existing algorithms on the bases of precision, recall, F-measure, execution time, and accuracy of results. From the experimental results, it is clear that the proposed hybrid clustering algorithm is more accurate, and has better precision, recall, and F-measure values.

Keywords:

clustering, Hadoop, big data, k-means, hierarchical
Received: 08 November 2018 Revised: 09 January 2019 Accepted: 12 January 2019 Published: 05 August 2019 Issue date: December 2019
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Publication history

Received: 08 November 2018
Revised: 09 January 2019
Accepted: 12 January 2019
Published: 05 August 2019
Issue date: December 2019

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© The author(s) 2019

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