Underground mining method selection is one of the most crucial decisions that should be made by mining engineers. Choosing a suitable underground mining method to carry out extraction from a mineral deposit is very important in terms of the economics, safety and the productivity of mining operations. The aim of this study is developing a fuzzy model to selection the optimum mining method by usi...

We show that prediction of travel time on a 28-km long highway section based on on-line travel time measurements with video is practicable by data mining and neuro-fuzzy methods. We introduce two new prediction models. The first one is a result of GUHA style data mining analysis and Total Fuzzy Similarity method, and the second one is a hierarchical model based on neuro-fuzzy modelling. Compari...

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...

Association rule mining is an important topic in data mining research. Many algorithms have been developed for such task and they typically assume that the underlying associations hidden in the data are stable over time. However, in real world domains, it is possible that the data characteristics and hence the associations change significantly over time. Existing data mining algorithms have not...

Data mining is a widely used approach for the transformation of large amounts of data to useful patterns and knowledge. Fuzzy association rules mining is a data mining technique which tries to nd association rules without the e ect of sharp boundary problems when data contains continuous and categorical attributes. Grid data mining is a new concept, which allows the data mining process to be de...

Data mining is concerned with developing algorithms and computational tools and techniques to help people extract patterns from data. In this paper an efficient data mining approach, which is based on fuzzy set theory and clonal selection algorithm, is proposed. The main motivation is to benefit from the global search performed by this kind of algorithms. Experimental results show the number of...

In 1998, Bonissone (1998) perceived soft computing to be a recently coined term, describing the symbiotic use of many emerging computing disciplines, amongst these Fuzzy Logic (introduced by Zadeh, 1965). They suggested that fuzzy logic gives us a language, with syntax and local semantics, in which we can translate our qualitative knowledge about the problem to be solved, with its main characte...

The growth of biomedical databases has seen a demand for data mining techniques to efficiently and effectively analyse the data contained within. One important consideration is the need to include expert’s opinions within the knowledge discovery process. However, this can be difficult to accomplish when such heuristics are presented in loosely defined, fuzzy terms. We present the use of an Adap...

In this paper we extend a previous definition of gradual dependence as a special kind of (crisp) association rule, in order to measure not only the existence of a tendency, but its strength. The new proposal is based on the idea of fuzzy association rule and the definition of variation strength in the degree of fulfilment of an imprecise property by different objects. We study the new semantics...

To optimize telecom service management, it is necessary that information about telecom services is highly related to the most popular telecom service. To this end, we propose an algorithm for mining targetoriented fuzzy correlation rules. In this paper, we show that by using the fuzzy statistics analysis and the data mining technology, the target-oriented fuzzy correlation rules can be obtained...