Abstract
On-line analytical (OLAP) is a data summarization and aggregation tool that helps simplify data analyzing where containing in the data warehouse. However, OLAP is some different with data mining tools, which discover the implicit patterns and interesting knowledge in large amount of databases. In this study, we propose to translate the frequent pattern tree structure into the 3-D multidimensional data structure. The frequent pattern tree is used for generating compact set of frequent patterns, so the 3-D multidimensional data structure, which is converted by FP-tree is only storage the frequent patterns. And then import the multidimensional data structure into the OLAP tool to discover the interesting knowledge. The efficiency is in three aspects: (1) because the frequent pattern tree is mining the complete set of frequent patterns that helps only analyzing the meaningful patterns in data warehouse. (2) It integrates OLAP with data mining and mining knowledge in multidimensional databases.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Pedersen, T.B., Jensen, C.S.: Multidimensional Data Modeling for Complex Data. In: Proceedings of ICDE (1999)
Surajit, C., Umeshwar, D.: An Overview of Data Warehousing and OLAP Technology. Newsletter ACM SIGMOD Record Homepage Archive (1997)
Keim, D.A.: Information Visualization and visual data mining. IEEE Transactions on Visualization and Computer Graphics (2002)
Yu, G., Chen, J., Zhu, L.: Data mining techniques for materials informatics: datasets preparing and applications. In: 2009 Second International Symposium on Knowledge Acquisition and Modeling (2004)
Han, J.W., Pei, J., Yin, Y.W., Mao, R.Y.: Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach. Data Mining and Knowledge Discovery (2004)
Gray, J., et al.: Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab and Sub-Totals. In: Proc. of ICDE, pp. 152–159 (1996)
Sawires, A.S., El Makky, N.M., Ahmed, K.M.: Multilevel Chunking of Multidimensional Arrays. In: IEEE International Conference on Computer Systems and Applications (2005)
Pourabbas, E., Shoshani, A.: The composite OLAP-Object Data Model: Removing an unnecessary Barrier. In: International Conference on Scientific and Statistical Database Management (2006)
Zheng, H.Z., Liu, G.Z., Zhan, D.C.: In: International Conference on Machine Learning and Cybernetics (2004)
Ge, T.j., Zdonik, S.: A*-tree: A Structure for Storage and Modeling of Uncertain Multidimensional Arrays. In: Proceedings of the VLDB Endowment (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Jin, L., Lim, J.Y., Kim, I.J., Cho, K.S., Kim, S.K., Kim, U.M. (2011). Mining Frequent Items in OLAP. In: Lee, C., Seigneur, JM., Park, J.J., Wagner, R.R. (eds) Secure and Trust Computing, Data Management, and Applications. STA 2011. Communications in Computer and Information Science, vol 187. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22365-5_4
Download citation
DOI: https://doi.org/10.1007/978-3-642-22365-5_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-22364-8
Online ISBN: 978-3-642-22365-5
eBook Packages: Computer ScienceComputer Science (R0)