Abstract
The decision-making process can be supported by many pioneering technologies such as Data Warehouse (DW), On-Line Analytical Processing (OLAP), and Data Mining (DM). Much research found in literature is aimed at integrating these popular research topics. In this chapter, we focus on discovering cyclic patterns from advanced multi-dimensional context, specially parallel hierarchies where more than one hierarchy is associated to given dimension in respect to several analytical purposes. Thus, we introduce a new framework for cyclic association rules mining from multiple hierarchies. To exemplify our proposal, an illustrative example is provided throughout the article. Finally, we perform intensive experiments on synthetic and real data to emphasize the interest of our approach.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
The data warehouse is related to pharmaceutical listed company. It is built using the available information at http://www.bvmt.com.tn/companies/?view=listed.
References
Agrawal, R., Imielinski, T., Swami, A.: Mining association rules between sets of items in large databases. In: Proceedings of the ACM SIGMOD International Conference on Management of data, pp. 207–216. ACM, New York (1993)
BenAhmed, E., Gouider, M.S.: Towards a new mechanism of extracting cyclic association rules based on partition aspect. In: Proceedings of the International Conference on Research Challenges in Information Science, pp. 69–78. IEEE, Nice (2010)
BenAhmed, E., Nabli, A., Gargouri, F.: Cyclic association rules: Coupling between dimensions with measures. In: Proceedings of the 23rd International Conference on Software Engineering and Knowledge Engineering, pp. 379–384. ACM, New York (2011)
BenAhmed, E., Nabli, A., Gargouri, F.: Cyclic association rules: Coupling multiple levels and parallel dimension hierarchies. In: Proceedings of the International Conference on Information and Knowledge Engineering, pp. 192–198. IEEE, USA (2011)
BenAhmed, E., Nabli, A., Gargouri, F.: Mining cyclic association rules from multidimensional knowledge. In: Proceedings of the 6th International Conference on Digital Information Management, pp. 12–17. IEEE, Melbourne (2011)
BenMessaoud, R., Boussaid, O., Rabaséda, S., Missaoui, R.: Enhanced mining of association rules from data cubes. In: Proceedings of the 9th International Workshop on Data Warehousing and OLAP, pp. 11–18. ACM, Nice (2006)
Chiang, D., Wang, C., Chen, S., Chen, C.: The cyclic model analysis on sequential patterns. IEEE Trans. Knowl. Data Eng. 21(11), 1617–1628 (2009)
Han, J., Fu, Y.: Discovery of multiple-level association rules from large databases. In: Proceedings of the International Conference on Very Large Data Bases, pp. 420–431. ACM, Switzerland (1995)
Han, J., Gong, W., Yin, Y.: Mining segment-wise periodic patterns in time-related databases. In: Proceedings of the International Conference on Knowledge Discovery and Data Mining, pp. 214–218. ACM, New York (1998)
Ienco, D., Pitarch, Y., Poncelet, P., Teisseire, M.: Towards an automatic construction of contextual attribute-value taxonomies. In: Proceedings of the the 27th Symposium On Applied Computing, pp. 113–118. ACM, New York (2012)
Jensen, C., Pedersen, T., Thomsen, C.: Multidimensional databases and data warehousing. Synthesis Lectures on Data Management, Morgan and Claypool Publishers (2010)
Kamber, M., Han, J., Chiang, J.: Metarule-guided mining of multi-dimensional association rules using data cubes. In: Proceedings of the International Conference on Knowledge Discovery and Data Mining, pp. 207–210. ACM, California (1997)
Kamber, M., Han, J., Chiang, J.: Qc-trees: an efficient summary structure for semantic olap. In: Proceedings of the International Conference on Management of Data, pp. 64–75. ACM, California (2003)
Ozden, B., Ramaswamy, S., Silberschatz, A.: Cyclic association rules. In: Proceedings of the Fourteenth International Conference on Data Engineering, pp. 412–421. IEEE, Florida (1998)
Plantevit, M., Laurent, A., Laurent, D., Teisseire, M., Choong, Y.: Mining multidimensional and multilevel sequential patterns. In: Transactions on Knowledge Discovery from Data. pp. 155–174. ACM, Florida (2010)
Srikant, R., Vu, Q., Agrawal, R.: Mining association rules with item constraints. In: International Confernce on Knowledge Discovery in Databases and Data Mining. pp. 67–73. ACM, California (1997)
Thuan, N.: Mining time pattern association rules in temporal database. In: Innovations and Advances in Computer Sciences and Engineering, Springer Netherlands, pp. 7-11 (2010)
Tjioe, H., Taniar, D.: Mining association rules in data warehouses. Int. J. Data Warehousing Mining (IJDWM) 1(3), 28–62 (2005)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Ben Ahmed, E., Nabli, A., Gargouri, F. (2015). On Line Mining of Cyclic Association Rules From Parallel Dimension Hierarchies. In: Abou-Nasr, M., Lessmann, S., Stahlbock, R., Weiss, G. (eds) Real World Data Mining Applications. Annals of Information Systems, vol 17. Springer, Cham. https://doi.org/10.1007/978-3-319-07812-0_3
Download citation
DOI: https://doi.org/10.1007/978-3-319-07812-0_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07811-3
Online ISBN: 978-3-319-07812-0
eBook Packages: Business and EconomicsBusiness and Management (R0)