Abstract
As an important data mining task, frequent pattern mining has drawn attention from many researchers. This has led to the development of many frequent pattern mining algorithms, which include Apriori-based, tree-based, and hyperlinked array structure-based algorithms, as well as vertical mining algorithms. Although these algorithms are efficient and popular, they also suffer from some drawbacks. To tackle these drawbacks, we present in this paper an alternative algorithm called B-mine that uses a bitwise approach to mine frequent patterns. Evaluation results show the space- and time-efficiency of B-mine for frequent pattern mining, as well as the practicality of B-mine for social network analysis and knowledge discovery from social networks.
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References
Aggarwal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proceedings of VLDB, pp. 487–399 (1994)
Cuzzocrea, A., Jiang, F., Lee, W., Leung, C.K.: Efficient frequent itemset mining from dense data streams. In: Chen, L., Jia, Y., Sellis, T., Liu, G. (eds.) APWeb 2014. LNCS, vol. 8709, pp. 593–601. Springer, Heidelberg (2014)
Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. In: Proceedings of ACM SIGMOD, pp. 1–12 (2000)
Leung, C.K., Carmichael, C.L.: Exploring social networks: a frequent pattern visualization approach. In: Proceedings of IEEE SocialCom, pp. 419–424 (2010)
Leung, C.K.-S., Carmichael, C.L., Teh, E.W.: Visual analytics of social networks: mining and visualizing co-authorship networks. In: Schmorrow, D.D., Fidopiastis, C.M. (eds.) FAC 2011. LNCS (LNAI), vol. 6780, pp. 335–345. Springer, Heidelberg (2011)
Leung, C.K., Jiang, F., Pazdor, A.G.M., Peddle, A.M.: Parallel social network mining for interesting ‘following’ patterns. Concurrency Comput. Pract. Experience (2016). doi:10.1002/cpe.3773
Leung, C.K., Tanbeer, S.K., Cameron, J.J.: Interactive discovery of influential friends from social networks. Soc. Netw. Anal. Min. 4(1), 13 (2014). doi:10.1007/s13278-014-0154-z. Article no. 154
Leung, C.K., Tanbeer, S.K., Cuzzocrea, A., Braun, P., MacKinnon, R.K.: Interactive mining of diverse social entities. KES J. 20(2), 97–111 (2016)
Lin, J.C., Gan, W., Fournier-Viger, P., Hong, T.: Mining weighted frequent itemsets with the recency constraint. In: Cheng, R., Cui, B., Zhang, Z., Cai, R., Xu, J. (eds.) APWeb 2015. LNCS, vol. 9313, pp. 635–646. Springer, Heidelberg (2015)
Pei, J., Han, J., Lu, H., Nishio, S., Tang, S., Yang, D.: H-mine: hyper-structure mining of frequent patterns in large databases. In: Proceedings of IEEE ICDM, pp. 441–448 (2001)
Rahman, Q.M., Fariha, A., Mandal, A., Ahmed, C.F., Leung, C.K.: A sliding window-based algorithm for detecting leaders from social network action streams. In: Proceedings of IEEE/WIC/ACM WI-IAT, vol. 1, pp. 133–136 (2015)
Schaal, M., O’Donovan, J., Smyth, B.: An analysis of topical proximity in the Twitter social graph. In: Aberer, K., Flache, A., Jager, W., Liu, L., Tang, J., Guéret, C. (eds.) SocInfo 2012. LNCS, vol. 7710, pp. 232–245. Springer, Heidelberg (2012)
Shenoy, P., Bhalotia, J.R., Bawa, M., Shah, D.: Turbo-charging vertical mining of large databases. In: Proceedings of ACM SIGMOD, pp. 22–33 (2000)
Tanbeer, S.K., Leung, C.K., Cameron, J.J.: Interactive mining of strong friends from social networks and its applications in e-commerce. J. Organ. Comput. Electron. Commer. (JOCEC) 24(2–3), 157–173 (2014)
Wang, K., Tang, L., Han, J., Liu, J.: Top down FP-growth for association rule mining. In: Chen, M.-S., Yu, P.S., Liu, B. (eds.) PAKDD 2002. LNCS (LNAI), vol. 2336, pp. 334–340. Springer, Heidelberg (2002)
Tong, W., Leung, C.K., Liu, D., Yu, J.: Probabilistic frequent pattern mining by PUH-Mine. In: Cheng, R., Cui, B., Zhang, Z., Cai, R., Xu, J. (eds.) APWeb 2015. LNCS, vol. 9313, pp. 768–780. Springer, Heidelberg (2015)
Xu, H., Yang, Y., Wang, L., Liu, W.: Node classification in social network via a factor graph model. In: Pei, J., Tseng, V.S., Cao, L., Motoda, H., Xu, G. (eds.) PAKDD 2013, Part I. LNCS (LNAI), vol. 7818, pp. 213–224. Springer, Heidelberg (2013)
Yang, X., Ghoting, A., Ruan, Y., Parthasarathy, S.: A framework for summarizing and analyzing Twitter feeds. In: Proceedings of ACM KDD, pp. 370–378 (2012)
Yuan, Q., Cong, G., Ma, Z., Sun, A., Magnenat-Thalmann, N.: Who, where, when and what: discover spatio-temporal topics for Twitter users. In: Proceedings of ACM KDD, pp. 605–613 (2013)
Zaki, M.J.: Scalable algorithms for association mining. IEEE TKDE 12(3), 372–390 (2000)
Zhang, Y., Pang, J.: Distance and friendship: a distance-based model for link prediction in social networks. In: Cheng, R., Cui, B., Zhang, Z., Cai, R., Xu, J. (eds.) APWeb 2015. LNCS, vol. 9313, pp. 55–66. Springer, Heidelberg (2015)
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This project is partially supported by NSERC (Canada) and University of Manitoba.
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Jiang, F., Leung, C.K., Zhang, H. (2016). B-mine: Frequent Pattern Mining and Its Application to Knowledge Discovery from Social Networks. In: Li, F., Shim, K., Zheng, K., Liu, G. (eds) Web Technologies and Applications. APWeb 2016. Lecture Notes in Computer Science(), vol 9931. Springer, Cham. https://doi.org/10.1007/978-3-319-45814-4_26
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DOI: https://doi.org/10.1007/978-3-319-45814-4_26
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