AN IMPROVED FREQUENT PATTERN-GROWTH APPROACH TO DISCOVER RARE ASSOCIATION RULES

R. Uday Kiran, P. Krishna Reddy

Abstract

In this paper we have proposed an improved approach to extract rare association rules. The association rules which involve rare items are called rare association rules. Mining rare association rules is difficult with single minimum support (minsup) based approaches like Apriori and FP-growth as they suffer from “rare item problem” dilemma. At high minsup, frequent patterns involving rare items will be missed and at low minsup, the number of frequent patterns explodes. To address “rare item problem”, efforts have been made in the literature by extending the “multiple minimum support” framework to both Apriori and FP-growth approaches. The approaches proposed by extending “multiple minimum support” framework to Apriori require multiple scans on the dataset and generate huge number of candidate patterns. The approach proposed by extending the “multiple minimum support” framework to FP-growth is relatively efficient than Apriori based approaches, but suffers from performance problems. In this paper, we have proposed an improved multiple minimum support based FP-growth approach by exploiting the notions such as “least minimum support” and “infrequent leaf node pruning”. Experimental results on both synthetic and real world datasets show that the proposed approach improves the performance over existing approaches.

References

  1. Agrawal, R. and Srikanth, R. (1994). Fast algorithms for mining association rules. In International Conference on Very Large Databases.
  2. B. Liu, W. H. and Ma, Y. (1999). Mining association rules with multiple minimum supports. In ACM Special Interest Group on Knowledge Discovery and Data Mining Explorations.
  3. G. Melli, R. Z. O. and Kitts, B. (2006). Introduction to the special issues on successful real-world data mining applications. In ACM Special Interest Group on Knowledge Discovery and Data Mining Explorations, volume 8, Issue 1.
  4. H. Jiawei, P. Jian, Y. Y. and Runying, M. (2004). Mining frequent patterns without candidate generation: A frequent-pattern tree approach. In ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery.
  5. J. Hipp, U. G. and Nakhaeizadeh, G. (2000). Algorithms for association rule mining a general survey and comparision. In ACM Special Interest Group on Knowledge Discovery and Data Mining, Volume 2, Issue 1.
  6. Kiran, R. U. and Reddy, P. K. (2009). An improved multiple minimum support based approach to mine rare association rules. In IEEE Symposium on Computational Intelligence and Data Mining.
  7. Mannila, H. (1997). Methods and problems in data mining. In International Conference on Database Theory.
  8. R. Agrawal, T. I. and Swami, A. (1993). Mining association rules between sets of items in large databases. In ACM Special Interest Group on Management Of Data.
  9. Weiss, G. M. (2004). Mining with rarity: A unifying framework. In ACM Special Interest Group on Knowledge Discovery and Data Mining Explorations.
  10. Weiss, S. and Kulikowski, C. A. (1991). Computer systems that learn: Classification and prediction models from statistics. In Neural Nets, Machine Learning, and Expert Systems. Morgan Kaufmann.
  11. Xu, R. (2005). Survey of clustering algorithms. In IEEE Transactions on Neural Networks.
  12. Ya-Han Hu, Y.-L. C. (2004). Mining association rules with multiple minimum supports: A new algorithm and a support tuning mechanism. In Decision Support Systems.
Download


Paper Citation


in Harvard Style

Uday Kiran R. and Krishna Reddy P. (2009). AN IMPROVED FREQUENT PATTERN-GROWTH APPROACH TO DISCOVER RARE ASSOCIATION RULES . In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2009) ISBN 978-989-674-011-5, pages 43-52. DOI: 10.5220/0002299600430052


in Bibtex Style

@conference{kdir09,
author={R. Uday Kiran and P. Krishna Reddy},
title={AN IMPROVED FREQUENT PATTERN-GROWTH APPROACH TO DISCOVER RARE ASSOCIATION RULES},
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2009)},
year={2009},
pages={43-52},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002299600430052},
isbn={978-989-674-011-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2009)
TI - AN IMPROVED FREQUENT PATTERN-GROWTH APPROACH TO DISCOVER RARE ASSOCIATION RULES
SN - 978-989-674-011-5
AU - Uday Kiran R.
AU - Krishna Reddy P.
PY - 2009
SP - 43
EP - 52
DO - 10.5220/0002299600430052