The increasing bulk of data generation in industrial and scientific applications has fostered practitioners’ interest in mining large amounts of unlabeled data in the form of continuous, high speed, and time-changing streams of information. An appealing field is association stream mining, which models dynamically complex domains via rules without assuming any a priori structure. Different from ...

Various fuzzy data models such as fuzzy relational databases, fuzzy object-oriented databases, fuzzy objectrelational databases and fuzzy XML have been proposed in the literature in order to represent and process fuzzy information in databases and XML. But little work has been done in modeling fuzzy data types. Actually in the fuzzy data models, each fuzzy value is associated with a fuzzy data ...

Traditional DEA models do not deal with imprecise data and assume that the data for all inputs and outputs are known exactly. Inverse DEA models can be used to estimate inputs for a DMU when some or all outputs and efficiency level of this DMU are increased or preserved. this paper studies the inverse DEA for fuzzy data. This paper proposes generalized inverse DEA in fuzzy data envelopment anal...

envelopment analysis (dea) is a very eective method to evaluate the relative eciency of decision-making units (dmus). dea models divided all dmus in two categories: ecient and inecientdmus, and don't able to discriminant between ecient dmus. on the other hand, the observedvalues of the input and output data in real-life problems are sometimes imprecise or vague, suchas interval data, ...

The classification of fuzzy data is considered as the most challenging areas of data analysis and the complexity of the procedures has been obstacle to the development of new methods for fuzzy data analysis. However, there are significant advances in modeling systems in which fuzzy data are available in the field of mathematical programming. In order to exploit the results of the researches on ...

in this paper, we deal with fuzzy random variables for inputs andoutputs in data envelopment analysis (dea). these variables are considered as fuzzyrandom flat lr numbers with known distribution. the problem is to find a method forconverting the imprecise chance-constrained dea model into a crisp one. this can bedone by first, defuzzification of imprecise probability by constructing a suitablem...

The paper presents a new approach to fuzzy classification in the case of missing data. Rough-fuzzy sets are incorporated into logical type neuro-fuzzy structures and a rough-neuro-fuzzy classifier is derived. Theorems which allow determining the structure of the rough-neuro-fuzzy classifier are given. Several experiments illustrating the performance of the roughneuro-fuzzy classifier working in...

This paper considers approaches to the computation of association rules for intuitionistic fuzzy data. Association can provide guidance assessing significant relationships that be determined while analyzing The approach uses cardinality sets a minimum and maximum range support confidence metrics. A new notation is used enable representation running example queries about desirable features vacat...