Abstract
Association Rule Mining (ARM) is a popular data mining technique that has been used to determine customer buying patterns. Although improving performance and efficiency of various ARM algorithms is important, determining Healthy Buying Patterns (HBP) from customer transactions and association rules is also important. This paper proposes a framework for mining fuzzy attributes to generate HBP and a method for analysing healthy buying patterns using ARM. Edible attributes are filtered from transactional input data by projections and are then converted to Required Daily Allowance (RDA) numeric values. Depending on a user query, primitive or hierarchical analysis of nutritional information is performed either from normal generated association rules or from a converted transactional database. Query and attribute representation can assume hierarchical or fuzzy values respectively. Our approach uses a general architecture for Healthy Association Rule Mining (HARM) and prototype support tool that implements the architecture. The paper concludes with experimental results and discussion on evaluating the proposed framework.
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., Imielinski, T., Swami, A.N.: Mining Association Rules Between Sets of Items in Large Databases. In: Proceedings of the 1993 ACM SIGMOD Conference on Management of Data, pp. 207–216 (1993)
Bodon, F.: A Fast Apriori Implementation. In: Proceedings of the IEEE ICDM Workshop on Frequent Itemset Mining Implementations, vol. 90 (2003)
Lee, C.-H., Chen, M.-S., Lin, C.-R.: Progressive Partition Miner: An Efficient Algorithm for Mining General Temporal Association Rules. IEEE Transactions on Knowledge and Data Engineering 15(4), 1004–1017 (2003)
Chen, G., Wei, Q.: Fuzzy Association Rules and the Extended Mining Algorithms, Information Sciences-Informatics and Computer Science. An International Journal archive 147(1-4), 201–228 (2002)
Au, W.-H., Chan, K.: Farm: A Data Mining System for Discovering Fuzzy Association Rules. In: Proceedings of the 18th IEEE Conference on Fuzzy Systems, pp. 1217–1222 (1999)
Srikant, R., Agrawal, R.: Mining Quantitative Association Rules in Large Relational Tables. In: Proceedings of ACM SIGMOD Conference on Management of Data, pp. 1–12. ACM Press, New York (1996)
Dubois, D., Hüllermeier, E., Prade, H.: A Systematic Approach to the Assessment of Fuzzy Association Rules. To appear in Data Mining and Knowledge Discovery Journal (2006)
Xie, D.W.: Fuzzy Association Rules discovered on Effective Reduced Database Algorithm. In: Proceedings of IEEE Conference on Fuzzy Systems (2005)
He, Y., Tang, Y., Zhang, Y.-Q., Synderraman, R.: Adaptive Fuzzy Association Rule Mining for Effective Decision Support in Biomedical Applications. International Journal Data Mining and Bioinformatics 1(1), 3–18 (2006)
Gyenesei, A.: A Fuzzy Approach for Mining Quantitative Association Rules. Acta Cybernetical 15(2), 305–320 (2001)
Klir, G.J., Yuan, B.: Fuzzy Sets and Fuzzy Logic. Theory and Applications. Prentice hall, Englewood Cliffs (1995)
Coenen, F., Leng, P., Goulbourne, G.: Tree Structures for Mining Association Rules. Journal of Data Mining and Knowledge Discovery 15(7), 391–398 (2004)
Wang, C., Tjortjis, C.: PRICES: An Efficient Algorithm for Mining Association Rules. In: Proceedings of the 5th Conference on Intelligent Data Engineering Automated Learning. Lecture Notes in Computer Science Series, vol. 3177, pp. 352–358. Springer, Heidelberg (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Muyeba, M., Khan, M.S., Malik, Z., Tjortjis, C. (2006). Towards Healthy Association Rule Mining (HARM): A Fuzzy Quantitative Approach. In: Corchado, E., Yin, H., Botti, V., Fyfe, C. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2006. IDEAL 2006. Lecture Notes in Computer Science, vol 4224. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11875581_121
Download citation
DOI: https://doi.org/10.1007/11875581_121
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45485-4
Online ISBN: 978-3-540-45487-8
eBook Packages: Computer ScienceComputer Science (R0)