Map-Reduce based Frequent Sub-Graph Extraction

AUTHORS

Ch. Sudhakar,Assistant Professor, CSE, Vignan’s Institute of Information Technology, Visakhapatnam. India
A.Siva Pavan,III B.Tech CSE, Vignan’s Institute of Information Technology, Visakhapatnam. India
N. Thirupathi Rao,Associate Professor, Vignan’s Institute of Information Technology, Visakhapatnam. India
Debnath Bhattacharyya,Professor, Vignan’s Institute of Information Technology, Visakhapatnam. India

ABSTRACT

Frequent subgraph extraction from a substantial number of small graphs is a crude activity for some, information mining applications. To extricate frequent subgraphs, existing systems need to identify countless which is super straight with the cardinality of the dataset. Given the huge developing volume of graph information, it is hard to play out the regular subgraph extraction on a unified machine proficiently. Along these lines, there is a need to explore how to effectively play out this extraction over expansive datasets utilizing MapReduce. Parallelizing existing strategies straightforwardly utilizing MapReduce does not yield great execution as it is hard to adjust the remaining task at hand among the figure hubs. This structure receives the MRFSE procedure to iteratively remove Frequent subgraphs, i.e., all incessant size-(i+1) subgraphs are created dependent on continuous size-I subgraphs at the ith emphasis utilizing a solitary MapReduce work. To productively separate successive subgraphs, arrangement and mining stage are utilized which incorporates isomorphism testing to wipe out copy designs. Frequent subgraphs extraction should be possible productively and effectively by utilizing a disseminated domain named Hadoop MapReduce structure.

 

KEYWORDS

MRFSE, Map reduce, Hadoop, Sub graph

REFERENCES

[1]     J. Ha., “Data mining: concepts and techniques,” Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, (2005)
[2]     A. Inokuchi, T. Washio, and H. Motoda, “An apriori-based algorithm for mining frequent substructures from graph data,” In PKDD, vol.10, pp.13-23, (2000)
[3]     M. Kuramochi and G. Karypis, “Frequent subgraph discovery,” In ICDM, pp.313-320, (2001)
[4]     X. Yan and J. Han, “gspan: Graph-based substructure pattern mining,” In ICDM, pp.721-724, (2002)
[5]     J. Cheng, Y. Ke, and W. Ng, “Efficient query processing on graph databases,” ACM Transactions on Database Syst., vol.34, no.1, pp.1-15, (2009)
[6]     J. Dean and S. Ghemawat, “Mapreduce: Simplified data processing on large clusters,” Communications in. ACM, vol. 51, pp.107-113, (2008)
[7]     F. Afrati, D. Fotakis, and J. Ullman, “Enumerating subgraph instances using map-reduce,” in Proceedings of. IEEE 29th International Conference on Data Engineering, Apr, pp.62-73, (2013)
[8]     Agrawal R. and Shafer J.C., “Parallel mining of association rules,” IEEE Transactions on KDE, vol.8, no.6, pp.962-969, (1996)

CITATION

  • APA:
    Sudhakar,C.& Pavan,A.S.& Rao,N.T.& Bhattacharyya,D.(2020). Map-Reduce based Frequent Sub-Graph Extraction. International Journal of Multimedia and Ubiquitous Engineering, 15(1), 27-34. 10.21742/IJMUE.2020.15.1.03
  • Harvard:
    Sudhakar,C., Pavan,A.S., Rao,N.T., Bhattacharyya,D.(2020). "Map-Reduce based Frequent Sub-Graph Extraction". International Journal of Multimedia and Ubiquitous Engineering, 15(1), pp.27-34. doi:10.21742/IJMUE.2020.15.1.03
  • IEEE:
    [1] C.Sudhakar, A.S.Pavan, N.T.Rao, D.Bhattacharyya, "Map-Reduce based Frequent Sub-Graph Extraction". International Journal of Multimedia and Ubiquitous Engineering, vol.15, no.1, pp.27-34, May. 2020
  • MLA:
    Sudhakar Ch., Pavan A.Siva, Rao N. Thirupathi and Bhattacharyya Debnath. "Map-Reduce based Frequent Sub-Graph Extraction". International Journal of Multimedia and Ubiquitous Engineering, vol.15, no.1, May. 2020, pp.27-34, doi:10.21742/IJMUE.2020.15.1.03

ISSUE INFO

  • Volume 15, No. 1, 2020
  • ISSN(p):1975-0080
  • ISSN(e):2652-1954
  • Published:May. 2020

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