Abstract
During the last ten years, many algorithms have been proposed to mine frequent itemsets. In order to fairly evaluate their behavior, the IEEE/ICDM Workshop on Frequent Itemset Mining Implementations (FIMI’03) has been recently organized. According to its analysis, kDCI++ is a state-of-the-art algorithm. However, it can be observed from the FIMI’03 experiments that its efficient behavior does not occur for low minimum supports, specially on sparse databases. Aiming at improving kDCI++ and making it even more competitive, we present the kDCI-3 algorithm. This proposal directly accesses candidates not only in the first iterations but specially in the third one, which represents, in general, the highest computational cost of kDCI++ for low minimum supports. Results have shown that kDCI-3 outperforms kDCI++ in the conducted experiments. When compared to other important algorithms, kDCI-3 enlarged the number of times kDCI++ presented the best behavior.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules. In: 20th VLDB Conference (1994)
Bastide, Y., Taouil, R., Pasquier, N., Stumme, G., Lakhal, L.: Mining Frequent Patterns with Counting Inference. ACM SIGKDD Explorations 2(2) (2000)
Goethals, B., Zaki, M.J.: Advances in Frequent Itemset Mining Implementations: Introduction to FIMI 2003. In: IEEE ICDM FIMI Workshop (2003)
Grahne, G., Zhu, J.: Efficiently Using Prefix Trees in Mining Frequent Itemsets. In: IEEE ICDM FIMI Workshop (2003)
Han, J., Pei, J., Yin, Y.: Mining Frequent Patterns without Candidate Generation. In: ACM SIGMOD Conference (2000)
Liu, J., Pan, Y., Wang, K., Han, J.: Mining Frequent Item Sets by Opportunistic Projection. In: 8th ACM SIGKDD Conference (2002)
Orlando, S., Palmerimi, P., Perego, R., Lucchese, C., Silvestri, F.: kDCI++: A Multi–Strategy Algorithm for Discovering Frequent Sets in Large Databases. In: IEEE ICDM FIMI Workshop (2003)
Orlando, S., Palmerimi, P., Perego, R.: Adaptive and Resource–Aware Mining of Frequent Sets. In: IEEE ICDM Conference (2002)
Park, J.S., Chen, M., Yu, P.S.: An Effective Hash-Based Algorithm for Mining Association Rules. In: ACM SIGMOD Conference (1995)
Pietracaprina, A., Zandolin, D.: Mining Frequent Itemsets using Patricia Tries. In: IEEE ICDM FIMI Workshop (2003)
Savasere, A., Omiecinski, E., Navathe, S.: An Efficient Algorithm for Mining Association Rules in Large Databases. In: 21th VLDB Conference (1995)
Toivonen, H.: Sampling Large Databases for Association Rules. In: 22th VLDB Conference (1996)
Uno, T., Asai, T., Uchida, Y., Arimura, H.: LCM: An Efficient Algorithm for Enumerating Frequent Closed Item Sets. In: IEEE ICDM FIMI Workshop (2003)
Zaki, M.J., Parthasarathy, S., Ogihara, M., Li, W.: New Algorithms for Fast Discovery of Association Rules. In: 3rd ACM SIGKDD Conference (1997)
Zheng, Z., Kohavi, R., Mason, L.: Real World Performance of Association Rule Algorithms. In: 7th ACM SIGKDD Conference (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Prado, A., Targa, C., Plastino, A. (2004). Improving Direct Counting for Frequent Itemset Mining. In: Kambayashi, Y., Mohania, M., Wöß, W. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2004. Lecture Notes in Computer Science, vol 3181. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30076-2_37
Download citation
DOI: https://doi.org/10.1007/978-3-540-30076-2_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22937-7
Online ISBN: 978-3-540-30076-2
eBook Packages: Springer Book Archive