Abstract
Since its inception, association rule mining has become one of the core data mining tasks, and has attracted tremendous interest among researchers and practitioners. Many efficient algorithms have been proposed in the literature, e.g., Apriori, Partition, DIC, for mining association rules in the context of marketbasket analysis. They are all based on apriori methods, i.e., pruning the itemset lattice, and requires multiple database accesses. However, research so far has mainly focused on mining over binary data, i.e., either an item is present in a transaction or not. Little attention was paid to mining over data where the quantity of items is considered. In this paper, we propose to address the problem of mining fuzzy association rules, by considering the quantity of items in the transactions. After the fuzzification of the transaction database, we apply a new efficient algorithm, called FARD (Fuzzy Association Rule Discovery), for mining fuzzy association rules. FARD is based on the pruning of the fuzzy concept lattice, and can be applied equally to classical or fuzzy databases, by scanning the database only once.
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
P. Adriaans and D. Zantinge. Data mining. Addion-Wesley Longman, 1997.
R. Agrawal, T. Imielinski, and A.Swami. Database mining: a performance perspective. IEEE Transactions on Knowledge and Data Engineering, 5 (6): 914–925, 1993.
R. Agrawal, T. Imielinski, and A. Swami. Mining Association Rules between sets of items in large Databases. ACM SIGMOD Records, pages 207–216, 1993.
R. Agrawal and J. Shafer. Parallel mining of association rules. IEEE Trans. on Knowledge and Data Engg, 8 (6): 962–969, 1996.
R. Agrawal and R. Skirant. Fast algorithms for mining association rules. In Proceedings of the 20th Intl. Conference on Very Large Databases, pages 478–499, June 1994.
R. Agrawal and R. Skirant. Mining sequential patterns. In Proceedings of International Conference on Data Engineering, 1995.
S. Brin, R. Motawni, and J. D. Ullman. Dynamic itemset counting and implication rules for market basket data. In Proceedings of the ACM SIGMOD Intl. Conference on Management of Data, pages 255–264, May 1997.
D. Cheung, V. Ng, A. Fu, and Y. Fu. Efficient mining of association rules in distributed databases. IEEE Trans. on Knowledge and Data Eng., 8 (6): 911–922, 1996.
B. Ganter and R. Wille. Formal Concept Analysis. Springer-Verlag, Heidelberg, 1999.
R. Godin and R. Missaoui. An incremental concept formation approach for learning from databases. Theoretical Computer Science, (133): 387–419, 1994.
R. Godin, R. Missaoui, and A. April. Experimental comparision of Galois lattice browsing with conventional information retrievel methods. Internat. J. Man-Machine studies, (38): 747–767, 1993.
E.-H. Han, G. Karypis, and V. Kumar. Scalable parallel data mining for association rules. In Proceedings of ACM SGMOD Conference Management of Data, pages 277–288, May 1997.
M. Holsheimer, M.Kersten, H. Manilla, and H. Toinoven. A perspective on databases and data mining. In Proceedings of 1st Intl. Conf. Knowledge Discovery and Data Mining, August 1995.
M. Houtsma and A. Swami. Set-oriented mining of association rules in relational datbases. In Proceedings of 11th Intl. Conf. on Data Engineering, 1995.
A. Jaoua, F. Alvi, S. Elloumi, and S. Ben Yahia. Galois connection in fuzzy binary relations: applications for discovering association rules and decision making. In Proceedings of the 5th Intl. Conference RELMICS’2000, pages 141–149, Canada, 10–14 January 2000.
H. Manilla and H. Toinoven. Discovering generalized episodes using minimal occurences. In Proceedings of 2nd Intl. Conf knowledge discovery and Data mining, 1996.
H. Manilla, H. Toinoven, and I. Verkamo. Efficient algorithms for discovering association rules. In AAAI Worshop on Knowledge Discovery in Databases, pages 181–192, July 1994.
H. Manilla, H. Toinoven, and I. Verkamo. Discovering frequent episodes in sequences. In Proceedings of 1st Intl. Conf. Knowledge Discovery and Data Mining, 1995.
J. Park, M. Chen, and P. Yu. An effective hash based algorithm for mining association rules. In Proceedings of the ACM SIGMOD Intl. Conference on Management of Data, pages 175–186, May 1995.
J. Park, M. Chen, and P. Yu. Efficient parallel data mining for association rules. In Proceedings of the ACM Intl. Conf. Information and Knowledge Management, pages 31–36, November 1995.
N. Pasquier, Y. Bastide, R. Touil, and L. Lakhal. Pruning closed itemset lattices for association rules. In Proceedings of the 14th Intl. Conference BDA, Hammamet, Tunisia, pages 177–196, December 1998.
A. Savarese, E. Omiecinski, and S. Navathe. An efficient algorithm for mining association rules in large databases. In Proceedings of the 21 th VLDB Conference, pages 432–444, September 1995.
R. Skirant and R. Agrawal. Mining sequential patterns: Generalizations and performance improvements. In Proceedings of Intl Conf. Extending Database Technology, March 1996.
H. Toinoven. Sampling large databases for association rules. In Proceedings of 22nd Intl. VLDB Conf, pages 134–145, September 1996.
R. Wille. Knowledge acquisition by methods of formal concept analysis. Nova Science, New York, 1989.
D. Yeung and E. Tsang. Weighted fuzzy production rules. Fuzzy Sets and Systems, 88: 299–313, 1997.
L. Zadeh. Fuzzy sets. Information and Control, (69): 338–353, June 1965.
M.Zaki, M. Ogihara, S. Pathasarathy, and W. Li. Parallel data mining for association rules on shared-memory processors. In Proc. Supercomputing’96 IEEE Computer Soc., Los Alamitos, 1996.
M. Zaki, S. Pathasarathy, M. Ogihara, and W. Li. Evaluation of sampling for data mining for association rules. In Proceedings of 7th Workshop Research Issues in Data Eng., April 1997.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Yahia, S.B., Jaoua, A. (2001). Discovering Knowledge from Fuzzy Concept Lattice. In: Kandel, A., Last, M., Bunke, H. (eds) Data Mining and Computational Intelligence. Studies in Fuzziness and Soft Computing, vol 68. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1825-3_7
Download citation
DOI: https://doi.org/10.1007/978-3-7908-1825-3_7
Publisher Name: Physica, Heidelberg
Print ISBN: 978-3-7908-2484-1
Online ISBN: 978-3-7908-1825-3
eBook Packages: Springer Book Archive