In essence, data mining consists of extracting knowledge from data. This paper proposes an evolutionary system for discovering fuzzy classi cation rules. Fuzzy logic is useful for data mining especially in the case for performing classi cation task. Three methods were used to extract fuzzy classi cation rules using Evolutionary Algorithms: (1) genetic selection small number of large number of f...

Feature selection is an important preprocessing step in data mining, which has an impact on both the runtime and the result quality of the subsequent processing steps. While there are many cases where hierarchic relations between features exist, most existing feature selection approaches are not capable of exploiting those relations. In this paper, we introduce a method for feature selection in...

In this paper the application of rule mining in order to review the effective factors on supplier selection is reviewed in the following three sections 1) criteria selecting and information gathering 2) performing association rule mining 3) validation and constituting rule base. Afterwards a few of applications of rule base is explained. Then, a numerical example is presented and analyzed by Cl...

Input selection is an important step in nonlinear regression modeling. By input selection, an interpretable model can be built with less computational cost. Input selection thus has drawn great attention in recent years. However, most available input selection methods are model-based. In this case, the input data selection is insensitive to changes. In this article, an effective model-free meth...

Input selection is an important step in nonlinear regression modeling. By input selection, an interpretable model can be built with less computational cost. Input selection thus has drawn great attention in recent years. However, most available input selection methods are model-based. In this case, the input data selection is insensitive to changes. In this article, an effective model-free meth...

In last few years there are major changes and evolution has been done on classification of data. As the application area of technology is increases the size of data also increases. Classification of data becomes difficult because of unbounded size and imbalance nature of data. Class imbalance problem become greatest issue in data mining. Imbalance problem occur where one of the two classes havi...

Data mining is an increasingly important means of knowledge acquisition for many applications in diverse fields such as biology, medicine, management, engineering, etc. When tackling a large-scale problem that involves a multitude of potentially relevant factors but lacking a precise formulation or mathematical characterization to allow formal approaches to solution, the available data collecte...

Mining shovels or excavators are basic equipment designed to dig and load material in surface mines. They are often considered the major material-handling equipment for high-production, low-cost operations (HPLC). The selection of an appropriate shovel for any HPLC operation is of critical importance because the overall production of the mine is measured in terms of material handled by a princi...

Frequent pattern mining is the process of finding a pattern (a set of items, subsequences, substructures, etc.) that occurs frequently in a data set. It was proposed in the context of frequent itemsets and association rule mining. Frequent pattern mining is used to find inherent regularities in data. What products were often purchased together? Its applications include basket data analysis, cro...