Nowadays companies measure suppliers on the basis of a variety of factors and criteria that affect the supplier's selection issue. This paper intended to identify the key effective criteria for selection of green suppliers through an efficient algorithm callediterative process mining or i-PM. Green data were collected first by reviewing the previous studies to identify various environmental cri...

A method to extract a fuzzy rule based system from a trained artificial neural network for classification is presented. The fuzzy system obtained is equivalent to the corresponding neural network. In the antecedents of the fuzzy rules, it uses the similarity between the input datum and the weight vectors. This implies rules highly understandable. Thus, both the fuzzy system and a simple analysi...

Typically, the behavior of adaptive systems is specified by a set of rules that are hidden somewhere in the system’s implementation. These rules deal with instances of the domain model. Our purpose is to specify the adaptive response of the system at a higher level (to be applied and reused for different domains or adaptive applications) in an explicit form, called adaptation language. For this...

In recent years interest has grown in “mining” large databases to extract novel and interesting information. Knowledge Discovery in Databases (KDD) has been recognised as an emerging research area. Association rules discovery is an important KDD technique for better data understanding. This paper proposes an enhancement with a memory efficient data structure of a quantitative approach to mine a...

Data sparseness in tagging highly inflectional languages with large tagsets and scarce training resources is a problem that cannot be addressed using only common tagging techniques. Tiered tagging is a two-stage technique that uses for tagging a smaller ”hidden” tagset and, in the second phase, recovers the original tagset using a lexicon and a set of hand-written rules. The recovering is possi...

The association rules, discovered by traditional support–confidence based algorithms, provide us with concise statements of potentially useful information hidden in databases. However, only considering the constraints of minimum support and minimum confidence is far from satisfying in many cases. In this paper, we propose a fuzzy method to formulate how interesting an association rule may be. I...

Data mining is a process that analyzes voluminous digital data in order to discover hidden but useful patterns from digital data. However, the discovering of such hidden patterns has statistical meaning and may often disclose some sensitive information. As a result, privacy becomes one of the prime concerns in the datamining research community. Since distributed association mining discovers ass...

We propose a simple but efficient method to extract rules from the radial basis function (RBF) neural network. Firstly, the data are classified by an RBF classifier. During training the RBF network, we allow for large overlaps between clusters corresponding to the same class to reduce the number of hidden neurons while maintaining classification accuracy. Secondly, centers of the kernel functio...

We propose a method combining relational-logic representations with deep neural network learning. Domain-specific knowledge is described through relational rules which may be handcrafted or learned. The relational rule-set serves as a template for unfolding possibly deep neural networks whose structures also reflect the structure of given training or testing examples. Different networks corresp...

This paper investigates the predictability of spot foreign exchange rate returns from past buy-sell signals of the simple technical trading rules by using the nearest neighbors and the feedforward network regressions. The optimal choices for nearest neighbors, hidden units in a feedforward network and the training set are determined by the cross validation method which minimizes the mean square...