Data mining, also known as knowledge discovery in databases, is the process of discovery potentially useful, hidden knowledge or relations among data from large databases. An important topic in data mining research is concerned with the discovery of association rules. The majority of databases are distributed nowadays. In this paper is presented an algorithm for mining fuzzy association rules f...

Peculiarity rules are a new class of rules which can be discovered by searching relevance among a relatively small number of peculiar data. Peculiarity oriented mining in multiple data sources is different from, and complementary to, existing approaches for discovering new, surprising, and interesting patterns hidden in data. A theoretical framework for peculiarity oriented mining is presented....

In this article, we study the mass spectrum of the scalar hidden charm and hidden bottom tetraquark states which consist of the axial-axial type and the vector-vector type diquark pairs with the QCD sum rules. PACS number: 12.39.Mk, 12.38.Lg

A new kind of nonlinear adaptive filter, the adaptive neural fuzzy filter (ANFF), based upon a neural network's learning ability and fuzzy if-then rule structure, is proposed in this paper. The ANFF is inherently a feedforward multilayered connectionist network which can learn by itself according to numerical training data or expert knowledge represented by fuzzy if-then rules. The adaptation h...

Modern software systems usually deal with several sorts (types) of data elements simultaneously. Some of these sorts, like integers, booleans, and so on, can be seen as having an immediate, direct nature and therefore are called visible, and they are contrasted with the others, like types of objects (in OO sense), which are called hidden sorts. A language used to specify such software system ha...

Artificial neural networks (ANNs) have been successfully applied to solve a variety of classification and function approximation problems. Although ANNs can generally predict better than decision trees for pattern classification problems, ANNs are often regarded as black boxes since their predictions cannot be explained clearly like those of decision trees. This paper presents a new algorithm, ...

Neural network classifiers are known to be able to learn very accurate models. In the recent past, researchers have even been able to train neural networks with multiple hidden layers (deep neural networks) more effectively and efficiently. However, the major downside of neural networks is that it is not trivial to understand the way how they derive their classification decisions. To solve this...

I this paper we consider workforce management in repair/maintenance environments in which repairmen are cross-trained fo affend more than one fype of machine. In this confext, we sfudy the machine-repairman problem wifh heferogeneous machines buf with partially cross-trained repairmen. We introduce simple repairmanassignment rules as well as machine-priority rules thaf are effecfive in mirumizi...

This paper presents a memory -based mo del of direct psychophysical scaling. The model is based on an extension of the cognitive architecture ACT-R and uses anchors that serve as prototypes for the stimuli classified within each response category. Using the ANCHOR model as a specific example, a general Bayesian framework is introduced. It provides principled methods for making data-based infere...