An Improved Pattern Mining Technique for Graph Pattern Analysis Using Novel Behavior of Artificial Bee Colony Algorithm
DOI:
https://doi.org/10.31449/inf.v45i5.3321Abstract
Rising data complexity and volume in the network has attracted researchers towards substructure analysis. Subgraph mining is an area that has gained remarkable attention in the last couple of years to offer an intelligent analysis of more massive graphs and complicated data structures. It has been observed that graph pattern mining faces issues regarding the matching ruleset and complex instruction set execution problem. This paper introduces modern-day intelligence architecture based on Swarm Intelligence that is cross-validated by supervised Machine learning mechanisms. A new behavior incorporated with a new inter and intra hive behavior is incorporated in Swarm based Artificial Bee Colony. The proposed work model is evaluated over two different datasets with more than 4900 nodes in the graph. The proposed framework is evaluated using True Detection Rate, False Detection Rate, precision, and F-Measure, demonstrating an average improvement of 9.8%, 8.35%, 8.35% and 9.15% against existing GraMi work that represent an enhanced performance of the proposed pattern mining technique.References
Chen, Y., Zhao, X., Lin, X., Wang, Y. and Guo, D., 2018. Efficient Mining of Frequent Patterns on Uncertain Graphs. IEEE Transactions on Knowledge and Data Engineering, 31(2), pp.287-300.
Elseidy, M., Abdelhamid, E., Skiadopoulos, S., & Kalnis, P. (2014). Grami: Frequent subgraph and pattern mining in a single large graph. Proceedings of the VLDB Endowment, 7(7), 517-528.
Ingalalli, V., Ienco, D. and Poncelet, P., 2018. Mining frequent subgraphs in multigraphs. Information Sciences, 451, pp.50-66.
Cheng, H., Yan, X., & Han, J. (2014). Mining graph patterns. In Frequent pattern mining (pp. 307-338). Springer, Cham.
Gu, Y., Gao, C., Wang, L., & Yu, G. (2016). Subgraph similarity maximal all-matching over a large uncertain graph. World Wide Web, 19(5), 755-782.
Yuan, Y., Wang, G., Chen, L., & Wang, H. (2012). Efficient subgraph similarity search on large probabilistic graph databases. Proceedings of the VLDB Endowment, 5(9), 800-811.
Li, J., Zou, Z., &Gao, H. (2012). Mining frequent subgraphs over uncertain graph databases under probabilistic semantics. The VLDB Journal, 21(6), 753-777
Rehman, S.U., Asghar, S. and Fong, S.J., 2018. Optimized and Frequent Subgraphs: How Are They Related?. IEEE Access, 6, pp.37237-37249.
Abdelhamid, E., Canim, M., Sadoghi, M., Bhattacharjee, B., Chang, Y. C., &Kalnis, P. (2017). Incremental frequent subgraph mining on large evolving graphs. IEEE Transactions on Knowledge and Data Engineering, 29(12), 2710-2723
Bhuiyan, M. and Hasan, M.A. (2015) An iterative mapreduce based frequent subgraph mining algorithm. IEEE Trans. Knowl. Data Eng., 27, 608–620.
Zhao, X., Chen, Y., Xiao, C., Ishikawa, Y. and Tang, J., 2016. Frequent subgraph mining based on Pregel. The Computer Journal, 59(8), pp.1113-1128.
Talukder, N. and Zaki, M.J., 2016. A distributed approach for graph mining in massive networks. Data Mining and Knowledge Discovery, 30(5), pp.1024-1052
Aridhi, S., & Nguifo, E. M. (2016). Big graph mining: Frameworks and techniques. Big Data Research, 6, 1-10.
Moussaoui, M., Zaghdoud, M. and Akaichi, J., 2018. A New Framework of Frequent Uncertain Subgraph Mining. Procedia Computer Science, 126, pp.413-422.
Jena, B., Khan, C., & Sunderraman, R. (2018, November). SparkFSM: A Highly Scalable Frequent Subgraph Mining Approach using Apache Spark. In 2018 IEEE International Conference on Data Mining Workshops (ICDMW) (pp. 990-997). IEEE.
Islam, M. A., Ahmed, C. F., Leung, C. K., & Hoi, C. S. (2018, June). WFSM-MaxPWS: an efficient approach for mining weighted frequent subgraphs from edge-weighted graph databases. In Pacific-Asia Conference on Knowledge Discovery and Data Mining (pp. 664-676). Springer, Cham.
Iyer, A. P., Liu, Z., Jin, X., Venkataraman, S., Braverman, V., & Stoica, I. (2018). {ASAP}: Fast, Approximate Graph Pattern Mining at Scale. In 13th {USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 18) (pp. 745-761).
Choi, H., Kim, J., Yoon, Y. and Moon, B.R., 2019. Investigation of incremental hybrid genetic algorithm with subgraph isomorphism problem. Swarm and Evolutionary Computation, 49, pp.75-86.
Preti, G., Lissandrini, M., Mottin, D., & Velegrakis, Y. (2019). Mining patterns in graphs with multiple weights. Distributed and Parallel Databases, 1-39.
Rao, B., & Mishra, S. (2019). An Approach to Detect Patterns (Sub-graphs) with Edge Weight in Graph Using Graph Mining Techniques. In Computational Intelligence in Data Mining (pp. 807-817). Springer, Singapore.
Li, L., Zhang, F., & Liu, G. (2019). Multi-fuzzy-objective graph pattern matching with big graph data. Journal of Database Management (JDM), 30(4), 24-40.
Ray, A., Holder, L. B., & Bifet, A. (2019). Efficient frequent subgraph mining on large streaming graphs. Intelligent Data Analysis, 23(1), 103-132.
Priyadarshini, S., & Rodda, S. (2020). Geometric Multi-Way Frequent Subgraph Mining Approach to a Single Large Database. In Smart Intelligent Computing and Applications (pp. 233-244). Springer, Singapore.
Le, N. T., Vo, B., Nguyen, L. B., Fujita, H., & Le, B. (2020). Mining weighted subgraphs in a single large graph. Information Sciences, 514, 149-165.
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