Abstract
Mining user behavior patterns in mobile environments is an emerging topic in data mining fields with wide applications. By integrating moving paths with purchasing transactions, one can find the sequential purchasing patterns with the moving paths, which are called mobile sequential patterns of the mobile users. Mobile sequential patterns can be applied not only for planning mobile commerce environments but also analyzing and managing online shopping websites. However, unit profits and purchased numbers of the items are not considered in traditional framework of mobile sequential pattern mining. Thus, the patterns with high utility (i.e., profit here) cannot be found. In view of this, we aim at integrating mobile data mining with utility mining for finding high utility mobile sequential patterns in this study. A novel algorithm called UMSP L (high Utility Mobile Sequential Pattern mining by a Level-wised method) is proposed to efficiently find high utility mobile sequential patterns. The experimental results show that the proposed algorithm has excellent performance under various system conditions.
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References
Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules. In: Int’l Conf. on Very Large Data Bases, pp. 487–499 (1994)
Agrawal, R., Srikant, R.: Mining Sequential Patterns. In: IEEE Int’l Conference 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 Transaction on Knowledge and Data Engineering 21(12), 1708–1721 (2009)
Chen, M.-S., Park, J.-S., Yu, P.S.: Efficient data mining for path traversal patterns. IEEE Transactions on Knowledge and Data Engineering 10(2), 209–221 (1998)
Chu, C.J., Tseng, V.S., Liang, T.: Mining Temporal Rare Utility Itemsets in Large Databases Using Relative Utility Thresholds. Int’l Journal of Innovative Computing Information and Control 4(11), 2775–2792 (2008)
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: Utility-Based Data Mining (2005)
Lu, E.H.-C., Tseng, V.S.: Mining cluster-based mobile sequential patterns in location- based service environments. In: IEEE Int’l Conf. on Mobile Data Management (2009)
Lu, E.H.-C., Huang, C.-W., Tseng, V.S.: Continuous Fastest Path Planning in Road Networks by Mining Real-Time Traffic Event Information. In: Int’l Symposium on Intelligent Informatics (2009)
Tseng, V.S., Lu, E.H.-C., Huang, C.-H.: Mining temporal mobile sequential patterns in location-based service environments. In: IEEE Int’l Conf. on Parallel and Distributed Systems (2007)
Tseng, V.S., Lin, W.C.: Mining sequential mobile access patterns efficiently in mobile web systems. In: Int’l Conf. on Advanced Information Networking and Applications, 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: ACM SIGKDD Conf. on Knowledge Discovery and Data Mining, pp. 253–262 (2010)
Yao, H., Hamilton, H.J.: Mining itemset utilities from transaction databases. Data & Knowledge Engineering 59, 603–626 (2006)
Yun, C.-H., Chen, M.-S.: Using pattern-join and purchase-combination for mining web transaction patterns in an electronic commerce environment. In: IEEE Annu. Int. Computer Software and Application Conference, pp. 99–104 (2000)
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)
Cao, L.: In-depth Behavior Understanding and Use: the Behavior Informatics Approach. Information Science 180(17), 3067–3085 (2010)
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Shie, BE., Hsiao, HF., Yu, P.S., Tseng, V.S. (2012). Discovering Valuable User Behavior Patterns in Mobile Commerce Environments. In: Cao, L., Huang, J.Z., Bailey, J., Koh, Y.S., Luo, J. (eds) New Frontiers in Applied Data Mining. PAKDD 2011. Lecture Notes in Computer Science(), vol 7104. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28320-8_7
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DOI: https://doi.org/10.1007/978-3-642-28320-8_7
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