The rough-set theory proposed by Pawlak, has been widely used in dealing with data classification problems. The original rough-set model is, however, quite sensitive to noisy data. Tzung thus proposed deals with the problem of producing a set of fuzzy certain and fuzzy possible rules from quantitative data with a predefined tolerance degree of uncertainty and misclassification. This model allow...

The present article dedicates itself to fuzzy modelling of data–inherent structures. In particular two main points are dealt with: the introduction of a fuzzy modelling framework and the elaboration of an automated, data–driven design strategy to model complex data–inherent structures within this framework. The innovation concerning the modelling framework lies in the fact that it is consistent...

The Fuzzy Logic and system are widely used in many fields during the last decades. Many tools and methods are developed to mine the data in the interdisciplinary science, which witnesses the novel discovery in Science and new technologies in Engineering. To exchange the ideas and research in East Asia, the 2015 International Conference on Fuzzy System and Data Mining (FSDM 2015) is held during ...

There are many applications dealing with incomplete data sets that take different approaches to making imputations for missing values. Most tackle the problem for numerical input variables in the data set. However, when there are two types of input variables, numerical and categorical, the state of the art has provided no clear solutions. This paper presents a proposal for handling incomplete n...

Frequent Itemset Mining is an important approach for Market Basket Analysis. Earlier, the frequent itemsets are determined based on the customer transactions of binary data. Recently, fuzzy data are used to determine the frequent itemsets because it provides the nature of frequent itemset ie. , it describes whether the frequent itemset consists of only highly purchased items or medium purchased...

Supplier evaluation and selection is a critical decision process for companies in order to gain competitive advantage. Decision models focusing on risk minimization have recently received increasing attention in supplier selection literature. In this paper, risk factors, which incorporate qualitative as well as quantitative data, are employed in fuzzy data envelopment analysis for obtaining eff...

Moved by the need increased for modeling of the fuzzy data, the success of the systems of exact generation of summary of data, we propose in this paper, a new approach of generation of summary from fuzzy data called “FuzzySaintEtiQ”. This approach is an extension of the SaintEtiQ model to support the fuzzy data. It presents the following optimizations such as 1) the minimization of the expert r...

The incorporation of temporal semantics into the traditional data mining techniques has caused the creation of a new area called Temporal Data Mining. This incorporation is especially necessary if we want to extract useful knowledge from dynamic domains, which are time-varying in nature. Related to this topic, we proposed in [11] an algorithm named TSET for mining temporal patterns (sequences) ...

A prediction model for methane production in a wastewater processing facility is presented. The model is built by data-mining algorithms based on industrial data collected on a daily basis. Because of many parameters available in this research, a subset of parameters is selected using importance analysis. Prediction results of methane production are presented in this paper. The model performanc...