Genetic algorithm based framework for mining fuzzy association rules

It is not an easy task to know a priori the most appropriate fuzzy sets that cover the domains of quantitative attributes for fuzzy association rules mining, simply because characteristics of quantitative data are in general unknown. Besides, it is unrealistic that the most appropriate fuzzy sets ca...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Fuzzy sets and systems Jg. 152; H. 3; S. 587 - 601
Hauptverfasser: Kaya, M., Alhajj, R.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Amsterdam Elsevier B.V 16.06.2005
Elsevier
Schlagworte:
Algorithmics. Computability. Computer arithmetics
Applied sciences
Association rules
Computer science; control theory; systems
CURE clustering algorithm
Data mining
Exact sciences and technology
Fuzzy sets
Genetic algorithms
Mathematical logic, foundations, set theory
Mathematics
Multivariate analysis
Probability and statistics
Quantitative attributes
Sciences and techniques of general use
Set theory
Statistics
Theoretical computing
CURE clustering algorithm
Fuzzy sets
Quantitative attributes
Data mining
Association rules
Genetic algorithms
Membership function
Generating function
Cluster analysis (statistics)
Association rule
Fuzzy set
Census
Genetic algorithm
ISSN:0165-0114, 1872-6801
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:It is not an easy task to know a priori the most appropriate fuzzy sets that cover the domains of quantitative attributes for fuzzy association rules mining, simply because characteristics of quantitative data are in general unknown. Besides, it is unrealistic that the most appropriate fuzzy sets can always be provided by domain experts. Motivated by this, in this paper we propose an automated method for mining fuzzy association rules. For this purpose, we first present a genetic algorithm (GA) based clustering method that adjusts centroids of the clusters, which are to be handled later as midpoints of triangular membership functions. Next, we give a different method for generating the membership functions by using Clustering Using Representatives (CURE) clustering algorithm, which is known as one of the most efficient clustering algorithms described in the literature. Finally, we compared the proposed GA-based approach with other approaches from the literature. Experiments conducted on 100K transactions from the US census in the year 2000 show that the proposed method exhibits a good performance in terms of execution time and interesting fuzzy association rules.
ISSN:0165-0114
1872-6801
DOI:10.1016/j.fss.2004.09.014