A Comparative Analysis of Parallel and Distributed FSM approaches on Large-scale Graph Data
S. Ajay Kumar1, Pellakuri Vidyullatha2

1S. Ajay Kumar, Research Scholar, Department of Computer Science & Engineering, KLEF, Vaddemswaram, Guntur Dist, (Andhra Pradesh), India.
2Pellakuri Vidyullatha, Associate Professor, Department of Computer Science & Engineering, KLEF, Vaddemswaram, Guntur Dist, (Andhra Pradesh), India.
Manuscript received on 25 March 2019 | Revised Manuscript received on 06 April 2019 | Manuscript Published on 18 April 2019 | PP: 642-648 | Volume-7 Issue-6S March 2019 | Retrieval Number: F03270376S19/2019©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Graph Mining has an explosive growth or increasing use of graphs in generating graph databases of real-world applications. It contains either a single large-scale graph or set of small size graphs. A Graph is used for complex dynamic modeling objects and they are often changing in nature i.e. it may increase or decrease in their size. Frequent subgraph mining plays a vital role in data mining, with an objective of extracting useful knowledge is presented in terms of repeated structures. Graph mining is used for the chemo-informatics, Bio-informatics, sentiment analysis, social human behavior analysis, financial network analysis, web analysis, wired and wireless networks. In this paper, we describe in detail survey on frequent subgraph mining algorithms, which are helpful for retrieving knowledge from complex dynamic objects. There are different types of large-scale parallel and distributed frameworks used for performing graph partitioning, FSM based on Apriori and pattern growth strategies, and large-scale graph processing techniques to achieve load balancing memory scalability, partitioning, load balancing, granularity and technical enhancement for future generations.
Keywords: Graph Partitioning, Frequent Subgraph Mining, Apriori, Pattern Growth, Parallel Framework.
Scope of the Article: Data Mining