Survey of Finding Frequent Patterns in Graph Mining: Algorithms and Techniques
Vijender Singh1, Deepak Garg2
1Vijender Singh, Department of Computer Science and Engineering, Thapar University, Patiala (Punjab), India.
2Dr. Deepak Garg, Professor, Department of Computer Science and Engineering, Thapar University, Patiala (Punjab), India.
Manuscript received on May 20, 2011. | Revised Manuscript received on June 10, 2011. | Manuscript published on July 05, 2011. | PP: 19-23 | Volume-1 Issue-3, July 2011. | Retrieval Number: C044051311
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© The Authors. Published By: 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: Graphs become increasingly important in modeling complicated structures, such as circuits, images, chemical compounds, protein structures, biological networks, social networks, the web, workflows, and XML documents. Many graph search algorithms have been developed in chemical informatics, computer vision, video indexing and text retrieval with the increasing demand on the analysis of large amounts of structured data; graph mining has become an active and important theme in data mining.
Keywords: Subgraphs, Graph Mining, gSpan.