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

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

σ Bangladesh University of Business and Technology

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

Reference Format

Zahra Farzanyar,Nick Cercone (2013). Efficient mining of frequent itemsets in social network data based on MapReduce framework.
Le Zhou,; Zhiyong Zhong,Jin Chang,; Junjie Li; Huang,J Shengzhong,Feng (2010). Balanced parallel FP-Growth with MapReduce.
M Manyika,B Chui,J Brown,R Bughin,C Dobbs,A Roxburgh,Byers (2011). Big data: The next frontier for innovation, competition, and productivity.
Rakesh Agrawal,Jerry Kiernan,Ramakrishnan Srikant,Yirong Xu (1994). Hippocratic Databases.
S Gole,B Tidke (2015). Frequent Itemset Mining for Big data in Social Media using ClusBig FIM algorithm.
D Khanaferov,C Lue,T Wang (2014). Social Network Data Mining Using Natural Language Processing and Density Based Clustering.
Jeffrey Dean,Sanjay Ghemawat (2004). MapReduce.
Zhigang Zhang,Genlin Ji,Mengmeng Tang (2013). MREclat: An Algorithm for Parallel Mining Frequent Itemsets.
Othman Yahya,Osman Hegazy,Ehab Ezat (2012). An efficient implementation of Apriori algorithm based on Hadoop-Mapreduce model.
Sandy Moens,Emin Aksehirli,Bart Goethals (2013). Frequent Itemset Mining for Big Data.
Usama Fayyad,Gregory Piatetsky-Shapiro,Padhraic Smyth (1996). The KDD process for extracting useful knowledge from volumes of data.
M Zaki,K Gouda (2003). Fast vertical mining using diffsets.
Ning Li,Li Zeng,Qing He,Zhongzhi Shi (2012). Parallel Implementation of Apriori Algorithm Based on MapReduce.
Jaliya Ekanayake,Hui Li,Bingjing Zhang,Thilina Gunarathne,Seung-Hee Bae,Judy Qiu,Geoffrey Fox (2010). Twister.
Zhiyong Ma,Juncheng Yang,Taixia Zhang,Fan Liu (2016). An Improved Eclat Algorithm for Mining Association Rules Based on Increased Search Strategy.
S Moens,E Aksehirli,B Goethals (2013). Frequent Itemset Mining for Big Data.
L Zhou,Z Zhong,J Chang,J Li,J Huang,S Feng (2010). Balanced parallel FP-Growth with MapReduce.
Dawen Xia,Yanhui Zhou,Zhuobo Rong,Zili Zhang,Ipfp (2013). An improved parallel FP-Growth Algorithm for Frequent Itemset Mining.
Suhel Hammoud (2011). MapReduce Network Enabled Algorithms for Classification Based on Association Rules.
Sudhakar Singh,Rakhi Garg,P Mishra (2014). Observations on factors affecting performance of MapReduce based Apriori on Hadoop cluster.

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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. 2016. \u201cComparative Analysis of MapReduce Framework for Efficient Frequent Itemset Mining in Social Network Data\u201d. 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
Pg. 45- 51

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

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Classification

C.1.4,C.2.1,C.2.4 J.4

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

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December 15, 2016

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Article Matrices

Total Score: 102

Country: Bangladesh

Subject: Global Journal of Computer Science and Technology - B: Cloud & Distributed

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

View Count (all-time): 245

Total Views (Real + Logic): 7438

Total Downloads (simulated): 1948

Publish Date: 2016 12, Thu

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Total Views: 7438

Total Downloads: 1948

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