Abstract
Class detection rules are mostly used for classifying new objects. Another possible usage is to describe a set of objects (a class) by the rules. Determinacy Analysis (DA) is a knowledge mining method with such purpose. Sets of rules are used to answer the questions “Who are they (objects of the class)?”, “How can we describe them?”. Rules found by different DA methods tend to contain some redundant information called zero factors. In this paper we show how zero factors are related to closed sets and minimal generators. We propose a new algorithm that extracts zero-factor-free rules and zero factors themselves, based on finding generators. Knowing zero factors gives to the analyst important additional knowledge for understanding the essence of the described set of objects (a class).
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
References
Bastide, Y., Pasquier, N., Taouil, R., Stumme, G., Lakhal, L.: Mining minimal non-redundant association rules using frequent closed itemsets. In: Lloyd, J., Dahl, V., Furbach, U., Kerber, M., Lau, K.K., Palamidessi, C., Pereira, L., Sagiv, Y., Stuckey, P. (eds.) Computational Logic—CL 2000, LNCS, vol. 1861, pp. 972–986. Springer, Berlin (2000)
Bastide, Y., Taouil, R., Pasquier, N., Stumme, G., Lakhal, L.: Mining frequent patterns with counting inference. SIGKDD Explor. Newsl. 2(2), 66–75 (2000)
Chesnokov, S.: Determination-analysis of social-economic data in dialogical regime (Preprint). All-Union Institute for Systems Research, Moscow (1980)
Chesnokov, S.: Determinacy analysis of social-economic data. Nauka, Moscow, Russia (1982)
Chesnokov, S.: Determinacy analysis of socio-economic data. Illustrative materials to lectures. Lecture 2: Rules. Lecture 3: Systems of rules (2002), lomonosov Moscow State University, Faculty of Economics, Moscow, unpublished (in Russian)
Jõgiste, L.: Prototyping of Zero-factor based DA. Master’s thesis, Tallinn University of Technology (2014)
Lind, G., Kuusik, R.: New developments for determinacy analysis: diclique-based approach. WSEAS Trans. Inf. Sci. Appl. 5(10), 1458–1469 (2008)
Lind, G., Kuusik, R.: Some problems in determinacy analysis approaches development. In: DMIN 2008, pp. 102–108. Las Vegas, Nevada (2008)
Pasquier, N., Bastide, Y., Taouil, R., Lakhal, L.: Pruning closed itemset lattices for association rules. In: BDA 1998, pp. 177–196. Hammamet, Tunisie (1998)
Quinlan, J.R.: Learning efficient classification procedures and their application to chess end games. In: Michalski, R., Carbonell, J., Mitchell, T. (eds.) Machine learning. An Artificial Intelligence Approach, pp. 463–482. Springer, Berlin (1984)
Quinlan, J.R.: Induction of decision trees. Mach. Learn. 1(1), 81–106 (1986)
UCI: Machine Learning Repository. http://archive.ics.uci.edu/ml/datasets/Nursery
Zaki, M.J., Hsiao, C.J.: CHARM: an efficient algorithm for closed itemset mining. SIAM 2002, 457–473 (2002)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Lind, G., Kuusik, R. (2016). Algorithm for Finding Zero Factor Free Rules. In: Gruca, A., Brachman, A., Kozielski, S., Czachórski, T. (eds) Man–Machine Interactions 4. Advances in Intelligent Systems and Computing, vol 391. Springer, Cham. https://doi.org/10.1007/978-3-319-23437-3_36
Download citation
DOI: https://doi.org/10.1007/978-3-319-23437-3_36
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-23436-6
Online ISBN: 978-3-319-23437-3
eBook Packages: EngineeringEngineering (R0)