Data mining and game sounds classification prerequisite to find a compact but effective set of features in the overall problem-solving process. As a preprocessing step of data mining, feature selection has tuned to be very efficient in reducing its dimensionality and removing irrelevant data at hand. In this paper we cast a feature selection problem on rough set theory and a conditional entropy in information theory and present an empirical study on feature analysis for classical instrument classification. An new definition of a significance of each feature using rough set theory based on rough entropy is proposed. Our results suggest that further feature analysis research is necessary in order to optimize feature selection and achieve better results for the musical instrument sound classification problem through Weka’s classifiers. The results show that the performance of the best 17 selected features among 37 features has 3.601 compared to 2.332 in standard deviation and 94.667 compared to 96.935 in average with four classifiers.

Variable precision rough set models have been successfully applied to problems whose domains are discrete values. However, there are many situations where discrete data is not available. When it comes to the problems with interval values, no variable prec

Data mining is widely used for turning huge amounts of data into useful information and knowledge in the information industry in recent years. When analyzing data set with continuous values in order to gain knowledge utilizing data mining, we often underg

Classification is an important area in a data mining. There are various ways in classification methodologies : the decision tree and the neural network, etc. Recently, Rough set theory has been presented as a method for classification. Rough set theory is a new approach in decision making in the presence of uncertainty and vagueness. In the process of constructing the tree, appropriate attributes have to be selected as nodes of the tree. In this paper, we present a new approach to selection of attributes for the construction of decision tree using the Rough set theory. The suggested method makes more simple classification rules in the decision tree and reduces the volume of the data to be treated.

Data mining is widely used for turning huge amounts of data into useful information and knowledge in the information industry in recent years. When analyzing data set with continuous values in order to gain knowledge utilizing data mining, we often undergo a process called discretization, which divides the attribute’s value into intervals. Such intervals from new values for the attribute allow to reduce the size of the data set. In addition, discretization based on rough set theory has the advantage of being easily applied. In this paper, we suggest a discretization algorithm based on Rough set and SOM(Self-Organizing Map) as a means of extracting valuable information from large data set, which can be employed even in the case where there lacks of professional knowledge for the field.