Abstract
This article presents a study on the techniques for detecting Emerging Sequential Patterns (ESPs) and the effectiveness of predictions made by ESPs in time-stamped datasets. ESPs are sequential patterns whose frequencies increase from one time-stamp dataset to another. ESPs capture emerging trends with time in sequential datasets and they are proposed for trend prediction. This work presents a study on the effectiveness of such predictions made by ESPs. Our experimental results show that, ESPs improve patterns’ re-occurrence prediction than frequent patterns, but the improvements are marginal. Further more, we note that both ESPs and frequent patterns do not fare well in predicting the continuous emergence of patterns with time. Hence, we conclude with suggestions on future works that will improve current ESPs definition to enable detect non-trivial and interesting ESPs which can help increase the precision of predicting future emerging patterns with ESPs.
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References
Adedoyin-Olowe, M., Gaber, M.M., Stahl, F.: TRCM: A Methodology for Temporal Analysis of Evolving Concepts in Twitter. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2013, Part II. LNCS (LNAI), vol. 7895, pp. 135–145. Springer, Heidelberg (2013)
Agrawal, R., Srikant, R.: Mining Sequential Patterns. In: 11th IEEE International Conference on Data Engineering, pp. 3–14. IEEE (1995)
Ayres, J., Flannick, J., Gehrke, J., Yiu, T.: Sequential Pattern Mining using a Bitmap Representation. In: 8th ACM SIGKDD International Conference on Knowledge Discovery, pp. 429–435. ACM (2002)
Boettcher, M.: Contrast and Change Mining. Wiley Interdisciplinary Reviews: Data Min. Knowl. Disc. 1(3), 215–230 (2011)
Chen, M.C., Chiu, A.L., Chang, H.H.: Mining Changes in Customer Behavior in Retail Marketing. Expert Syst. Appl. 28(4), 773–781 (2005)
Cho, Y.B., Cho, Y.H., Kim, S.H.: Mining Changes in Customer Buying Behavior for Collaborative Recommendations. Expert Syst. Appl. 28(2), 359–369 (2005)
Dong, G., Li, J.: Efficient Mining of Emerging Patterns: Discovering Trends and Differences. In: 5th ACM SIGKDD International Conference on Knowledge Discovery, pp. 43–52. ACM (1999)
Garofalakis, M.N., Rastogi, R., Shim, K.: SPIRIT: Sequential Pattern Mining with Regular Expression Constraints. In: 25th International Conference on Very Large Data Bases, pp. 7–10. Morgan Kaufmann (1999)
Gomariz, A., Campos, M., Marin, R., Goethals, B.: ClaSP: An Efficient Algorithm for Mining Frequent Closed Sequences. In: Pei, J., Tseng, V.S., Cao, L., Motoda, H., Xu, G. (eds.) PAKDD 2013, Part I. LNCS (LNAI), vol. 7818, pp. 50–61. Springer, Heidelberg (2013)
Huang, T.C.K.: Mining the Change of Customer Behavior in Fuzzy Time-Interval Sequential Patterns. Appl. Soft Comput. 12(3), 1068–1086 (2012)
Huang, Z., Gan, C., Lu, X., Huan, H.: Mining the Changes of Medical Behaviors for Clinical Pathways. In: 14th World Congress on Medical and Health Informatics, pp. 117–121 (2013)
Li, J., Liu, H., Downing, J.R., Yeoh, A.E.J., Wong, L.: Simple Rules Underlying Gene Expression Profiles of More than Six Subtypes of Acute Lymphoblastic Leukemia (ALL) Patients. Bioinformatics 19(1), 71–78 (2003)
Li, J., Dong, G., Ramamohanarao, K., Wong, L.: Deeps: A New Instance-Based Lazy Discovery and Classification System. Machine Learning 54(2), 99–124 (2004)
Li, J., Dong, G., Ramamohanarao, K.: Making Use of the Most Expressive Jumping Emerging Patterns for Classification. Knowl. Inf. Syst. 3(2), 131–145 (2001)
Li, J., Wong, L.: Emerging Patterns and Gene Expression Data. Genome Informatics, 3–13 (2001)
Liu, B., Hsu, W., Han, H.S., Xia, Y.: Mining Changes for Real-Life Applications. In: Kambayashi, Y., Mohania, M., Tjoa, A.M. (eds.) DaWaK 2000. LNCS, vol. 1874, pp. 337–346. Springer, Heidelberg (2000)
Mooney, C.H., Roddick, J.F.: Sequential Pattern Mining–Approaches and Algorithms. ACM Comput. Surv. 45(2), 19:1–19:39 (2013)
Shih, M.J., Liu, D.R., Hsu, M.L.: Mining Changes in Patent Trends for Competitive Intelligence. In: Washio, T., Suzuki, E., Ting, K.M., Inokuchi, A. (eds.) PAKDD 2008. LNCS (LNAI), vol. 5012, pp. 999–1005. Springer, Heidelberg (2008)
Shih, M.J., Liu, D.R., Hsu, M.L.: Discovering Competitive Intelligence by Mining Changes in Patent Trends. Trends. Expert Syst. Appl. 37(4), 2882–2890 (2010)
Song, H.S., Kim, S.H.: Mining the Change of Customer Behavior in an Internet Shopping Mall. Expert Syst. Appl. 21(3), 157–168 (2001)
Szathmary, L.: Méthodes Symboliques de Fouille de Données avec la Plate-forme Coron (Doctoral dissertation. Université Henri Poincaré-Nancy I) (2006)
Tsai, C.Y., Lo, C.C., Lin, C.W.: A Time-Interval Sequential Pattern Change Detection Method. International. J. Inf. Tech. Decis. 10(01), 83–108 (2011)
Tsai, C.Y., Shieh, Y.C.: A Change Detection Method for Sequential Patterns. Decis. Support Syst. 46(2), 501–511 (2009)
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Nofong, V.M., Liu, J., Li, J. (2014). A Study on the Applications of Emerging Sequential Patterns. In: Wang, H., Sharaf, M.A. (eds) Databases Theory and Applications. ADC 2014. Lecture Notes in Computer Science, vol 8506. Springer, Cham. https://doi.org/10.1007/978-3-319-08608-8_6
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DOI: https://doi.org/10.1007/978-3-319-08608-8_6
Publisher Name: Springer, Cham
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