Integration of two fuzzy data mining methods

Horvath, Tomas; Krajči, Stanislav

Abstract

The cluster analysis and the formal concept analysis are both used to identify significiant groups of similar objects. Rice & Siff's algorithm for the clustering joins these two methods in the case where the values of an object-attribute model are 1 or 0 and often reduce an amount of concepts. We use a certain type of fuzzification of a concept lattice for generalization of this clustering algorithm in the fuzzy case. For the purpose of finding dependencies between the objects in the clusters we use our method of the induction of generalized annotated programs based on multiple using of the crisp inductive logic programming. Since our model contains fuzzy data, it should have work with a fuzzy background knowledge and a fuzzy set of examples - which are not divided clearly into positive and negative classes, but there is a monotone hierarchy (degree, preference) of more or less positive / negative examples. We have made experiments on data describing business competitiveness of Slovak companies.

Citation

Horvath, T., & Krajči, S. (2004). Integration of two fuzzy data mining methods. Neural Network World, 14(5), 391-402

• Journal Article Type: Article
• Acceptance Date: Mar 1, 2004
• Publication Date: Jun 1, 2004
• Deposit Date: Apr 8, 2024
• Print ISSN: 1210-0552
• Electronic ISSN: 2336-4335
• Publisher: Czech Technical University in Prague, Faculty of Transportation Sciences
• Peer Reviewed: Peer Reviewed
• Volume: 14
• Issue: 5
• Pages: 391-402
• Keywords: fuzzy data, clustering, concept lattices, inductive logic programming, graded classification, fuzzy and annotated programs
• Public URL: http://researchrepository.napier.ac.uk/Output/3588364
• Publisher URL: http://www.nnw.cz/obsahy04.html