Abstract
Earlier research has resulted in the production of an ‘all-rules’ algorithm for data-mining that produces all conjunctive rules of above given confidence and coverage thresholds. While this is a useful tool, it may produce a large number of rules. This paper describes the application of two clustering algorithms to these rules, in order to identify sets of similar rules and to better understand the data.
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
Bayardo, R., Agrawal, R., Gunopulos, D.: Constraint-Based RuleMining in Large, Dense Databases. In: Proceedings of the 15th International Conference on Data Engineering, pp. 188–197. IEEE, Los Alamitos (1999)
de la Iglesia, B., Philpott, M.S., Bagnall, A.J., Rayward-Smith, V.J.: Data Mining Rules Using Multi-Objective Evolutionary Algorithms. In: Proceedings of 2003 IEEE Congress on Evolutionary Computation (2003)
Richards, G., Rayward-Smith, V.J.: Discovery of association rules in tabular data. In: Proceedings of IEEE First International Conference on Data Mining, San Jose, California, USA, pp. 465–473. IEEE Computer Society, Los Alamitos (2001)
Blake, C.L., Merz, C.J.: UCI Repository of machine learning databases (1998), http://www.ics.uci.edu/~mlearn/MLRepository.html
Kaufman, L., Rousseeuw, P.J.: Finding Groups in Data: An Introduction to Cluster Analysis. Wiley series in probability and mathematical statistics. John Wiley and Sons Inc., Chichester (1990)
Jaccard, P.: Étude comparative de la distribution florale dans une portion des Alpes et des Jura. Bulletin de la Société Vaudoise de la Sciences Naturelles 37, 547–579 (1901)
Gower, J.C., Legendre, P.: Metric and Euclidean properties of dissimilarity coefficients. Journal of Classification 3, 5–48 (1986)
MacQueen, J.B.: Some Methods for Classification and Analysis of Multivariate Observations. In: Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 281–297. University of California Press, California (1967)
Ng, R.T., Han, J.: CLARANS: A Method for Clustering Objects for Spatial Data Mining. IEEE Transactions on Knowledge and Data Engineering 14(5), 1003–1016 (2002)
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
Reynolds, A.P., Richards, G., Rayward-Smith, V.J. (2004). The Application of K-Medoids and PAM to the Clustering of Rules. In: Yang, Z.R., Yin, H., Everson, R.M. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2004. IDEAL 2004. Lecture Notes in Computer Science, vol 3177. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28651-6_25
Download citation
DOI: https://doi.org/10.1007/978-3-540-28651-6_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22881-3
Online ISBN: 978-3-540-28651-6
eBook Packages: Springer Book Archive