Abstract
Data warehouses contain large volumes of time-variant data stored to help analysis. Despite the evolution of OLAP analysis tools and methods, it is still impossible for decision makers to find data mining tools taking the specificity of the data (e.g. multidimensionality, hierarchies, time-variant) into account. In this paper, we propose an original method to automatically extract sequential patterns with respect to hierarchies. This method extracts patterns that describe the inner trends by displaying patterns that either go from precise knowledge to general knowledge or go from general knowledge to precise knowledge. For instance, one rule exhibited could be data contain first many sales of coke in Paris and lemonade in London for the same date, followed by a large number of sales of soft drinks in Europe, which is said to be divergent (as precise results like coke precede general ones like soft drinks). On the opposite, rules like data contain first many sales of soft drinks in Europe and chips in London for the same date, followed by a large number of sales of coke in Paris are said to be convergent. In this paper, we define the concepts related to this original method as well as the associated algorithms. The experiments which we carried out show the interest of our proposal.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Agrawal, R., Srikant, R.: Mining sequential patterns. In: Yu, P.S., Chen, A.L.P. (eds.) ICDE 1995, pp. 3–14. IEEE Computer Society, Los Alamitos (1995)
Ayres, J., Flannick, J., Gehrke, J., Yiu, T.: Sequential pattern mining using a bitmap representation. In: KDD, pp. 429–435 (2002)
Dartnell, C., Sallantin, J.: Assisting scientific discovery with an adaptive problem solver. In: Discovery Science, pp. 99–112 (2005)
Gardner, M.: Mathematical games. Scientific American (1959)
Masseglia, F., Cathala, F., Poncelet, P.: The psp approach for mining sequential patterns. In: Zytkow, J.M., Quafafou, M. (eds.) PKDD 1998. LNCS, vol. 1510, pp. 176–184. Springer, Heidelberg (1998)
Pei, J., Han, J., Mortazavi-Asl, B., Wang, J., 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) (2004)
Pinto, H., Han, J., Pei, J., Wang, K., Chen, Q., Dayal, U.: Multi-dimensional sequential pattern mining. In: CIKM 2001, pp. 81–88. ACM, New York (2001)
Plantevit, M., Choong, Y.W., Laurent, A., Laurent, D., Teisseire, M.: M2SP: Mining Sequential Patterns Among Several Dimensions. In: Jorge, A.M., Torgo, L., Brazdil, P.B., Camacho, R., Gama, J. (eds.) PKDD 2005. LNCS (LNAI), vol. 3721, pp. 205–216. Springer, Heidelberg (2005)
Plantevit, M., Laurent, A., Teisseire, M.: Hype: mining hierarchical sequential patterns. In: DOLAP, pp. 19–26 (2006)
Yu, C.-C., Chen, Y.-L.: Mining sequential patterns from multidimensional sequence data. IEEE Transactions on Knowledge and Data Engineering 17(1), 136–140 (2005)
Zaki, M.J.: Spade: An efficient algorithm for mining frequent sequences. Machine Learning 42(1/2), 31–60 (2001)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Plantevit, M., Laurent, A., Teisseire, M. (2008). Up and Down: Mining Multidimensional Sequential Patterns Using Hierarchies. In: Song, IY., Eder, J., Nguyen, T.M. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2008. Lecture Notes in Computer Science, vol 5182. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85836-2_15
Download citation
DOI: https://doi.org/10.1007/978-3-540-85836-2_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-85835-5
Online ISBN: 978-3-540-85836-2
eBook Packages: Computer ScienceComputer Science (R0)