In this paper, Fuzzy concept hierarchies are used for multi-level association rule mining from large datasets via Attribute-Oriented Induction approach [1]. In this the process of fuzzy hierarchical induction approach is used and extends it with two new characteristics which improve applicability of the original approach in data mining. The proposed drilling-down approach of fuzzy induction mod...

During the last ten years, data mining, also known as knowledge discovery in databases, has established its position as a prominent and important research area. Mining association rules is one of the important research problems in data mining. Many algorithms have been proposed to nd association rules in large databases containing both categorical and quantitative attributes. We generalize this...

In this paper we present how to extract fuzzy association rules involving both the presence and the absence of items using a fuzzy rule mining procedure introduced by the authors in previous works. The rule mining procedure is based on the GUHA logical model, fuzzified via a recently proposed representation of gradualness. We present some results obtained with real datasets.

Efficient algorithms for discovering association rules from binary data already exist. However, most of real world databases are not boolean. The problem of expanding these algorithms to handle variety types of data such as quantitative data has been attracted the attention of many researchers. In this paper we provide a comparison of existing algorithms for generating association rules from qu...

Qualitative modeling may be applied when knowledge about a system is only available in linguistic form. The knowledge might be processed by a dynamic fuzzy system consisting of a rule base and an inference method modeling human reasoning. Conventional fuzzy inference methods do not consider this association to human reasoning and therefore are not suitable for the dynamic processing of linguist...

This paper surveys some genetic-fuzzy data mining techniques for mining both membership functions and fuzzy association rules. The motivation from crisp mining to fuzzy mining will be first described. Three types of genetic-fuzzy data mining approaches are then described according to the utilized methods and different mining problems, including Integrated GeneticFuzzy approaches for items with ...