Abstract
In this paper we propose an original approach to apply data mining algorithms, namely decision tree-based methods, taking into account not only the size of processed databases but also the processing time. The key idea consists in constructing a decision tree, within the DBMS, using bitmap indices. Indeed bitmap indices have many useful properties such as the count and bit-wise operations. We will show that these operations are efficient to build decision trees. In addition, by using bitmap indices, we don’t need to access raw data. This implies clear improvements in terms of processing time.
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
Agrawal, R., Mannila, H., Srikant, R., et al.: Fast discovery of association rules. Advances in Kowledge Discovery and Data Mining, 307–328 (1996)
Bentayeb, F., Darmont, J.: Decision tree modeling with relational views. In: Hacid, M.-S., Raś, Z.W., Zighed, D.A., Kodratoff, Y. (eds.) ISMIS 2002. LNCS (LNAI), vol. 2366, pp. 423–431. Springer, Heidelberg (2002)
Bentayeb, F., Darmont, J., Udréa, C.: Efficient integration of data mining techniques in dbmss. In: 8th International Database Engineering and Applications Symposium (IDEAS 2004), Portugal (2004)
Chauchat, J.H., Rakotomalala, R.: A new sampling strategy for building decision trees from large databases. In: 7th Conference of the International Federation of Classification Societies (IFCS 2000), Belgium (2000)
Chaudhuri, S.: Data mining and database systems: Where is the intersection? Data Engineering Bulletin 21(1), 4–8 (1998)
Dunkel, B., Soparkar, N.: Data organization and access for efficient data mining. In: ICDE, pp. 522–529 (1999)
Gehrke, J., Ramakrishnan, R., Ganti, V.: Rainforest - a framework for fast decision tree construction of large datasets. In: 24th International Conference on Very Large Data Bases (VLDB 1998), USA (1998)
Geist, I., Sattler, K.U.: Towards data mining operators in database systems: Algebra and implementation. In: 2nd International Workshop on Databases, Documents, and Information Fusion, DBFusion 2002 (2002)
Han, J., Fu, Y., Wang, W., Koperski, K., Zaiane, O.: DMQL: A data mining query language for relational databases. In: SIGMOD 1996 Workshop on Research Issues in Data Mining and Knowledge Discovery (DMKD 1996), Canada (1996)
Imielinski, T., Virmani, A.: Msql: A query language for database mining. DataMining and Knowledge Discovery: An International Journal 3, 373–408 (1999)
Liu, H., Motoda, H.: Feature Selection for knowledge discovery and data mining. Kluwer Academic Publishers, Dordrecht (1998)
Meo, R., Psaila, G., Ceri, S.: A new SQL-like operator for mining association rules. The VLDB Journal, 122–133 (1996)
Oracle. Oracle 9i data mining. White paper (June 2001)
Quinlan, J.R.: Induction of decision trees. Machine Learning 1, 81–106 (1986)
Ramesh, G., Maniatty, W., Zaki, M.: Indexing and data access methods for database mining (2001)
Sarawagi, S., Thomas, S., Agrawal, R.: Integrating mining with relational database systems: Alternatives and implications. In: ACM SIGMOD International Conference on Management of Data (SIGMOD 1998), USA (1998)
Soni, S., Tang, Z., Yang, J.: Performance study microsoft data mining algorithms. Technical report, Microsoft Corp. (2001)
Toivonen, H.: Sampling large databases for association rules. In: Proc. 1996 Int. Conf. Very Large Data Bases (1996)
Wang, H., Zaniolo, C., Luo, C.R.: Atlas: a small but complete sql extension for data mining and data streams. In: 29th VLDB Conference, Germany (2003)
Zighed, D.A., Rakotomalala, R.: Sipina-w(c) for windows: User’s guide. Technical report, ERIC laboratory, University of Lyon 2, France (1996)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Favre, C., Bentayeb, F. (2005). Bitmap Index-Based Decision Trees. In: Hacid, MS., Murray, N.V., Raś, Z.W., Tsumoto, S. (eds) Foundations of Intelligent Systems. ISMIS 2005. Lecture Notes in Computer Science(), vol 3488. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11425274_7
Download citation
DOI: https://doi.org/10.1007/11425274_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25878-0
Online ISBN: 978-3-540-31949-8
eBook Packages: Computer ScienceComputer Science (R0)