An Enhancement of Grami Based on Threshold Policy for Pattern Big Graphs
Shriya Sahu1, Meenu Chawla2, Nilay Khare3
1Shriya Sahu, Department of Computer Science & Engineering, Maulana Azad National Institute of Technology, Bhopal (M.P.) India.
2Dr. Meenu Chawla, Department of Computer Science & Engineering, Maulana Azad National Institute of Technology, Bhopal (M.P.) India.
3Dr. Nilay Khare, Department of Computer Science & Engineering, Maulana Azad National Institute of Technology, Bhopal (M.P.) India.
Manuscript received on December 15, 2019. | Revised Manuscript received on December 20, 2019. | Manuscript published on January 10, 2020. | PP: 2943-2949 | Volume-9 Issue-3, January 2020. | Retrieval Number: C9106019320/2020©BEIESP | DOI: 10.35940/ijitee.C9106.019320
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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: Pattern Mining is the key mechanism to manage large scale data element. Frequent subgraph mining (FSM) considers isomorphism which is a subprocess of pattern mining is a well-studied problem in the data mining. Graphs are considered as a standard structure in many domains such as protein-protein interaction network in biological networks, wired or wireless interconnection networks, web data, etc. FSM is the task of finding all frequent subgraphs from a given database i.e. a single big graph or database of many graphs, whose support is greater than the given threshold value. Many databases consider small graphs for solving complex problems. The classification of graph depends upon the application requirement. A good mining architecture may prevent a lot of memory and time. This paper follows the Grami structure for the analysis of frequent subgraph mining and also introduces the 20% threshold policy for the enhancement of the directed pattern graphs. The constraint satisfaction problem (CSP) has been discussed and analyzed using the Grami approach. The proposed model is compared to Grami on twitter dataset based on the evaluation of time and memory consumed. The proposed algorithm shows an improvement of 3-4 % for both the parameters. The results show that the performance of Grami approach has been improved which shows a 6.6% reduction in time and 21% improvement in memory consumption using the proposed approach.
Keywords: Big Graphs, Grami, Pattern Mining, Subgraph Mining
Scope of the Article: Graph Algorithms and Graph Drawing