IJCCT

g-FSG Approach for Finding Frequent Sub Graph

---

---

Abstract

Informally, a graph is set of nodes, pairs of which might be connected by edges. In a wide array of disciplines, data can be intuitively cast into this format. For example, computer networks consist of routers/computers (nodes) and the links (edges) between them. Social networks consist of individuals and their interconnections (which could be business relationships or kinship or trust, etc.) Protein interaction networks link proteins which must work together to perform some particular biological function. Ecological food webs link species with predator-prey relationships. In these and many other fields, graphs are seemingly ubiquitous. The problems of detecting abnormalities (outliers) in a given graph and of generating synthetic but realistic graphs have received considerable attention recently. Both are tightly coupled to the problem of finding the distinguishing characteristics of real-world graphs, that is, the patterns that show up frequently in such graphs and can thus be considered as marks of realism. A good generator will create graphs which match these patterns. In this paper we present gFSG, a computationally efficient algorithm for finding frequent patterns corresponding to geometric sub graphs in a large collection of geometric graphs. gFSG is able to discover geometric sub graphs that can be rotation, scaling, and translation invariant, and it can accommodate inherent errors on the coordinates of the vertices.

Recommended Citation

[1] R. Agrawal, R. Srikant, Fast algorithms for mining association rules, in: J.B. Bocca, M. Jarke, C. Zaniolo (Eds.), Proceedings of the 20th International Conference on Very Large Data Bases (VLDB), Morgan Kaufmann, Los Altos, CA, 1994, pp. 487–499. [1] J. Han, J. Pei, Y. Yin, Mining frequent patterns without candidate generation, in: Proceedings of the ACM SIGMOD International Conference on Management of Data, Dallas, TX, 2000, pp. 1–12. [2] X. Yan, J. Han, gSpan: graph-based substructure pattern mining, in: Proceedings of the 2002 IEEE International Conference on Data Mining (ICDM), 2002, pp. 721–724. [3] X. Yan, J. Han, CloseGraph: mining closed frequent graph patterns, in: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2003), 2003, pp. 286– 295. [4] J. Huan, W. Wang, J. Prins, Efficient mining of frequent subgraph in the presence of isomorphism, in: Proceedings of the 2003 IEEE International Conference on Data Mining (ICDM’03), 2003, pp. 549– 552. [5] X. Wang, J.T.L. Wang, D. Shasha, B.A. Shapiro, I. Rigoutsos, K. Zhang, Finding patterns in three dimensional graphs: algorithms and applications to scientific data mining, IEEE Trans. Knowl. Data Eng. 14 (4) (2002) 731–749. [6] M.J. Zaki, Scalable algorithms for association mining, IEEE Trans. Knowl. Data Eng. 12 (2) (2000) 372–390. [7] G. Cong, L. Yi, B. Liu, K. Wang, Discovrering frequent substructures from hierarchical semi-structured data, in: Proceedings of the Second SIAM International Conference on Data Mining (SDM-2002), 2002. [8] K. Wang, H. Liu, Discovering structural association of semistructured data, IEEE Trans. Knowl. Data Eng. 12 (2000) 353–371. [10] T. Asai, K. Abe, S. Kawasoe, H. Arimura, H. Sakamoto, S. Arikawa, Efficient substructure discovery from large semistructured data, in: Proceedings of the Second SIAM International Conference on Data Mining (SDM’02), [11] Sadhana Priyadarshini, ,Debahuti Mishra, An Insight into Graph Database, Proceeding of National Conference on Computational Biology,NCCB-09,(2009)Page 68-71. [12] Sadhana Priyadarshini , Debahuti Mishra, An approach to Graph Mining using Apriori Algorithm Proceeding of First International Conference on Advance computing and Communication,ICACC-2010,Page 69-72.