Abstract
Maximal Biclique Enumeration (MBE) has become a central task to many data mining problems arising in Web mining, shopping recommendation, business and bioinformatics. It is crucial to accelerate the sequential MBE that is the basis of the parallel MBE. In this paper, we present an efficient algorithm for maximal biclique enumeration (EMBE) on bipartite graphs in a depth-first manner and need not to store previously computed maximal bicliques in memory for duplicate detection. Previous studies have shown that reduce the number of checking closure condition and manage the child nodes are huge challenges for generating all maximal bicliques. In this paper, we propose that (1) an efficient implementation for pruning technique based on the stack when checking nodes are closed or not, (2) a new method to manage the expansion child nodes through a global data structure.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Alexe, G., Alexe, S., Crama, Y., Foldes, S., Hammer, P.L., Simeone, B.: Consensus algorithms for the generation of all maximal bicliques. Discrete Appl. Math. 145(1), 11–21 (2004)
Brin, S., Motwani, R., Ullman, J.D., Tsur, S.: Dynamic itemset counting and implication rules for market basket data. ACM SIGMOD Rec. 26(2), 255–264 (1997)
Flake, G.W., Lawrence, S., Giles, C.L.: Efficient identification of web communities. In: Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 150–160. ACM (2000)
Gély, A., Nourine, L., Sadi, B.: Enumeration aspects of maximal cliques and bicliques. Discrete Appl. Math. 157(7), 1447–1459 (2009)
Grahne, G., Zhu, J.: Fast algorithms for frequent itemset mining using FP-trees. IEEE Trans. Knowl. Data Eng. 17(10), 1347–1362 (2005)
Hashem, T., Karim, M.R., Samiullah, M., Ahmed, C.F.: An efficient dynamic superset bit-vector approach for mining frequent closed itemsets and their lattice structure. Expert Syst. Appl. 67, 252–271 (2017)
Huan, J., Wang, W., Prins, J., Yang, J.: Spin: mining maximal frequent subgraphs from graph databases. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 581–586. ACM (2004)
Inokuchi, A., Washio, T., Motoda, H.: Complete mining of frequent patterns from graphs: mining graph data. Mach. Learn. 50(3), 321–354 (2003)
Li, J., Liu, G., Li, H., Wong, L.: Maximal biclique subgraphs and closed pattern pairs of the adjacency matrix: a one-to-one correspondence and mining algorithms. IEEE Trans. Knowl. Data Eng. 19(12), 1625–1637 (2007)
Malgrange, Y.: Recherche des sous-matrices premières d’une matrice à coefficients binaires. Applications à certains problèmes de graphe. In: Proceedings of the Deuxième Congrès de l’AFCALTI, pp. 231–242 (1962)
Mukherjee, A.P., Tirthapura, S.: Enumerating maximal bicliques from a large graph using mapreduce. IEEE Trans. Serv. Comput. 10(5), 771–784 (2017)
Nagarajan, N., Kingsford, C.: Uncovering genomic reassortments among influenza strains by enumerating maximal bicliques. In: 2008 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2008, pp. 223–230. IEEE (2008)
Pasquier, N., Bastide, Y., Taouil, R., Lakhal, L.: Discovering frequent closed itemsets for association rules. In: International Conference on Database Theory, pp. 398–416. Springer (1999)
Pei, J., Han, J., Mao, R., et al.: CLOSET: an efficient algorithm for mining frequent closed itemsets. In: ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, vol. 4, pp. 21–30 (2000)
Selvan, S., Nataraj, R.: Efficient mining of large maximal bicliques from 3D symmetric adjacency matrix. IEEE Trans. Knowl. Data Eng. 22(12), 1797–1802 (2010)
Shin, K., Hooi, B., Faloutsos, C.: M-Zoom: fast dense-block detection in tensors with quality guarantees. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 264–280. Springer (2016)
Sinthuja, M., Puviarasan, N., Aruna, P.: Geo map visualization for frequent purchaser in online shopping database using an algorithm LP-growth for mining closed frequent itemsets. Procedia Comput. Sci. 132, 1512–1522 (2018)
Uno, T., Asai, T., Uchida, Y., Arimura, H.: LCM: an efficient algorithm for enumerating frequent closed item sets. In: Fimi, vol. 90. Citeseer (2003)
Uno, T., Kiyomi, M., Arimura, H.: LCM ver. 2: efficient mining algorithms for frequent/closed/maximal itemsets. In: Fimi, vol. 126 (2004)
Wang, J., Han, J., Pei, J.: CLOSET+: searching for the best strategies for mining frequent closed itemsets. In: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 236–245. ACM (2003)
Yan, X., Han, J.: CloseGraph: mining closed frequent graph patterns. In: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 286–295. ACM (2003)
Zaki, M.J., Hsiao, C.J.: CHARM: an efficient algorithm for closed itemset mining. In: Proceedings of the 2002 SIAM International Conference on Data Mining, pp. 457–473. SIAM (2002)
Zhong-Ji, F., Ming-Xue, L., Xiao-Xin, H., Xiao-Hui, H., Xin, Z.: Efficient algorithm for extreme maximal biclique mining in cognitive frequency decision making. In: 2011 IEEE 3rd International Conference on Communication Software and Networks (ICCSN), pp. 25–30. IEEE (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Qin, C., Liao, M., Liang, Y., Zheng, C. (2020). Efficient Algorithm for Maximal Biclique Enumeration on Bipartite Graphs. In: Liu, Y., Wang, L., Zhao, L., Yu, Z. (eds) Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. ICNC-FSKD 2019. Advances in Intelligent Systems and Computing, vol 1075. Springer, Cham. https://doi.org/10.1007/978-3-030-32591-6_1
Download citation
DOI: https://doi.org/10.1007/978-3-030-32591-6_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-32590-9
Online ISBN: 978-3-030-32591-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)