Comparative Analysis of MapReduce Framework for Efficient Frequent Itemset Mining in Social Network Data

Suman Saha; Md. Syful Islam Mahfuz

Comparative Analysis of MapReduce Framework for Efficient Frequent Itemset Mining in Social Network Data

Suman Saha * §

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Md. Syful Islam Mahfuz

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University of Chittagong

Comparative Analysis of MapReduce Framework for Efficient Frequent Itemset Mining in Social Network Data

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Abstract

Social networking sites are the virtual community for sharing information among the people. It raises its pularity tremendously over the past few years. Many social networking sites like Twitter, Facebook, WhatsApp, Instragram, LinkedIn generates tremendous amount data. Mining such huge amount of data can be very useful. Frequent itemset mining plays a significant role to extract knowledge from the dataset. Traditional frequent itemsets method is ineffective to process this exponential growth of data almost terabytes on a single computer. Map Reduce framework is a programming model that has emerged for mining such huge amount of data in parallel fashion. In this paper we have discussed how different MapReduce techniques can be used for mining frequent itemsets and compared each other’s to infer greater scalability and speed in order to find out the meaningful information from large datasets.

References

20 Cites in Article

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Suman Saha, Md. Syful Islam Mahfuz. 2016. "Comparative Analysis of MapReduce Framework for Efficient Frequent Itemset Mining in Social Network Data". Global Journal of Computer Science and Technology - B: Cloud & Distributed GJCST-B Volume 16 (GJCST Volume 16 Issue B3).

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GJCST Volume 16 Issue B3

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

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Classification

C.1.4

C.2.1

C.2.4 J.4

Submission ReceivedDecember 16, 2015
Peer Review Double Blind
Handling Editor
Accepted January 2, 2016
Published January 15, 2016

Version of record

v1.2

Issue date

December 15, 2016

Language

English

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Article Matrices

Total Score: 142

Country: Bangladesh

Subject: Global Journal of Computer Science and Technology

Authors: Suman Saha, Md. Syful Islam Mahfuz (PhD/Dr. count: 0)

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Publish Date: 2016 01, Fri

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