Abstract
Mining user behaviors in mobile environments is an emerging and important topic in data mining fields. Previous researches have combined moving paths and purchase transactions to find mobile sequential patterns. However, these patterns cannot reflect actual profits of items in transaction databases. In this work, we explore a new problem of mining high utility mobile sequential patterns by integrating mobile data mining with utility mining. To the best of our knowledge, this is the first work that combines mobility patterns with high utility patterns to find high utility mobile sequential patterns, which are mobile sequential patterns with their utilities. Two tree-based methods are proposed for mining high utility mobile sequential patterns. A series of analyses on the performance of the two algorithms are conducted through experimental evaluations. The results show that the proposed algorithms deliver better performance than the state-of-the-art one under various conditions.
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References
Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules. In: Proc. of the 20th Int’l. Conf. on Very Large Data Bases, pp. 487–499 (1994)
Agrawal, R., Srikant, R.: Mining Sequential Patterns. In: Proc. of 11th Int’l. Conf. on Data Mining, pp. 3–14 (1995)
Ahmed, C.F., Tanbeer, S.K., Jeong, B.-S., Lee, Y.-K.: Efficient Tree Structures for High Utility Pattern Mining in Incremental Databases. IEEE Trans. on Knowledge and Data Engineering 21(12), 1708–1721 (2009)
Chan, R., Yang, Q., Shen, Y.: Mining High Utility Itemsets. In: Proc. of Third IEEE Int’l Conf. on Data Mining, pp. 19–26 (November 2003)
Han, J., Pei, J., Yin, Y.: Mining Frequent Patterns without Candidate Generation. In: Proc. of the ACM-SIGMOD International Conference on Management of Data, pp. 1–12 (2000)
Lee, S.C., Paik, J., Ok, J., Song, I., Kim, U.M.: Efficient Mining of User Behaviors by Temporal Mobile Access Patterns. Int’l. Journal of Computer Science Security 7(2), 285–291 (2007)
Li, Y.-C., Yeh, J.-S., Chang, C.-C.: Isolated Items Discarding Strategy for Discovering High Utility Itemsets. Data & Knowledge Engineering 64(1), 198–217 (2008)
Liu, Y., Liao, W.-K., Choudhary, A.: A Fast High Utility Itemsets Mining Algorithm. In: Proc. of Utility-Based Data Mining (2005)
Lu, E.H.-C., Tseng, V.S.: Mining Cluster-based Mobile Sequential Patterns in Location-based Service Environments. In: Proc. of IEEE Int’l. Conf. on Mobile Data Management (2009)
Pei, J., Han, J., Mortazavi-Asl, B., Pinto, H., Chen, Q., Dayal, U., Hsu, M.C.: Mining Sequential Patterns by Pattern-Growth: The PrefixSpan Approach. IEEE Transactions on Knowledge and Data Engineering 16(10) (October 2004)
Tseng, V.S., Lin, W.C.: Mining Sequential Mobile Access Patterns Efficiently in Mobile Web Systems. In: Proc. of the 19th Int’l. Conf. on Advanced Information Networking and Applications, Taipei, Taiwan, pp. 867–871 (2005)
Tseng, V.S., Wu, C.W., Shie, B.-E., Yu, P.S.: UP-Growth: An Efficient Algorithm for High Utility Itemsets Mining. In: Proc. of the 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2010), Washington, DC, USA (July 2010)
Yen, S.-J., Lee, Y.-S.: Mining High Utility Quantitative Association Rules. In: Song, I.-Y., Eder, J., Nguyen, T.M. (eds.) DaWaK 2007. LNCS, vol. 4654, pp. 283–292. Springer, Heidelberg (2007)
Yun, C.-H., Chen, M.-S.: Mining Mobile Sequential Patterns in a Mobile Commerce Environment. IEEE Transactions on Systems, Man, and Cybernetics-Part C: Applications and Reviews 37(2) (2007)
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Shie, BE., Hsiao, HF., Tseng, V.S., Yu, P.S. (2011). Mining High Utility Mobile Sequential Patterns in Mobile Commerce Environments. In: Yu, J.X., Kim, M.H., Unland, R. (eds) Database Systems for Advanced Applications. DASFAA 2011. Lecture Notes in Computer Science, vol 6587. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20149-3_18
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DOI: https://doi.org/10.1007/978-3-642-20149-3_18
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