Social Mining to Improve the Computational Efficiency Using MapReduce
Ms. Babbitha.M, Dr. Angelina Geetha, Mr. Mohammed Jaffer.A.R, ,
Affiliations M.Tech, Software Engineering (CSE), B.S. AbdurRahman University, Chennai, IndiaAssociate Professor, Computer Science and Engineering, B.S. AbdurRahman University, Chennai, IndiaM.Tech, Software Engineering (CSE), B.S. AbdurRahman University, Chennai, India
Graphs are widely used in large scale social network analysis. Graph mining increasingly important in modelling complicated
structures such as circuits, images, web, biological networks and social networks. The major problems occur in this graph mining are
computational efficiency (CE) and frequent sub graph mining (FSM). Computational Efficiency describes the extent to which the time, effort or
efficiency which use computing technology in information processing. Frequent Subgraph Mining is the mechanism of candidate generation
without duplicates. FSM faces the problem on counting the instances of the patterns in the dataset and counting of instances for graphs. The main
objective of this project is to address CE and FSM problems. The paper cited in the reference proposes an algorithm called Mirage algorithm to
solve queries using sub graph mining. The proposed work focuses on enhancing An Iterative MapReduce based Frequent Subgraph Mining
Algorithm (MIRAGE) to consider optimum computational efficiency. The test data to be considered for this mining algorithm can be from any
domains such as medical, text and social dataâ€™s (twitter).The major contributions are: an iterative MapReduce based frequent subgraph mining
algorithm called MIRAGE used to address the frequent subgraph mining problem. Computational Efficiency will be increased through MIRAGE
algorithm over Matrix Vector Multiplication. Performance of the MIRAGE will be demonstrated through different synthetic as well as real world
datasets. The main aim is to improvise the existing algorithm to enhance Computational Efficiency.
Ms. Babbitha.M,Dr. Angelina Geetha,Mr. Mohammed Jaffer.A.R."Social Mining to Improve the Computational Efficiency Using MapReduce". International Journal of Computer Engineering In Research Trends (IJCERT) ,ISSN:2349-7084 ,Vol.2, Issue 05,pp.288-292, May - 2015, URL :https://ijcert.org/ems/ijcert_papers/V2I53.pdf,
Keywords : Computational Efficiency, Data Mining, Frequent SubgraphMining, Graphs, Map Reduce, Text Mining, Social Networks.
 Mansurul A Bhuiyan and Mohammad Al
Hasan, â€•MIRAGE: An Iterative MapReduce based
Frequent Subgraph Mining Algorithmâ€–, ACM
Computing Research Repository, arXiv: 1307.5894,
Volume 1, 2013.
 Yi-Chen Lo, Hung-CheLai, Cheng-Te Li and
Shou-De Lin,â€– Mining and Generating Large Scaled
Social Networks via MapReduceâ€–, Springer-Verlag
Advances in Social Networks Analysis and Mining, pp -
 SabaSehrish, Grant Mackey, Pengju Shang, Jun
Wang and John Bent,â€–Supporting HPC Analytics
Applications with Access Patterns Using Data
Restructuringand Data-Centric Scheduling
TechniquesinMapReduceâ€– IEEE Transactions on Parallel
and Distributed Systems, Volume 24, 2013.
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