In this chapter we discuss the theory and foundational issues in data mining, describe data mining methods and algorithms, and review data mining applications. A section is devoted to summarizing the state of rough sets as related to data mining of real-world databases.
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Rough set theory is a new method that deals with vagueness and uncertainty emphasized in decision making. Data mining is a discipline that has an important contribution to data analysis, discovery of new meaningful knowledge, and autonomous decision making. The rough set theory offers a viable approach for decision rule extraction from data.This paper, introduces the fundamental concepts of rough set theory and other aspects of data mining, a discussion of data representation with rough set theory including pairs of attribute-value blocks, information tables reducts, indiscernibility relation and decision tables. Additionally, the rough set approach to lower and upper approximations and certain possible rule sets concepts are introduced. Finally, some description about applications of the data mining system with rough set theory is included.
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Proceedings of the World Congress on …
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Knowledge Discovery in Databases (KDD) is the nontrivial extraction of implicit, previously unknown and potentially useful information from data. Data preprocessing is a step of the KDD process that reduces the complexity of the data and offers better conditions to subsequent analysis. Rough sets theory, where sets are approximated using elementary sets, is another approach for developing methods for the KDD process. In this doctoral Thesis, we propose new algorithms based on Rough sets theory for three data preprocessing steps: Discretization, feature selection, and instance selection. In Discretization, continuous features are transformed into new categorical features. This is required for some KDD algorithms working strictly with categorical features. In Feature selection, the new subset of features leads to a new dataset of lower dimension, where it is easier to perform a KDD task. When a dataset is very large, an instance selection process is required to decrease the computatio.
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Proceedings of the …
Using Rough Sets as Tools for Knowledge Discovery Ning Shan, Wojciech Ziarko, Howard J. Hamilton and Nick Cercone Department of Computer Science, University of Regina Regina, Saskatchewan, Canada $4S 0A2 E-Mail: @cs.uregina.ca .
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This book constitutes the refereed proceedings of the 12th International Conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, RSFDGrC 2009, held in Delhi, India in December 2009 in conjunction with the Third International Conference on Pattern Recognition and Machine Intelligence, PReMI 2009. RSFDGrC 2009 is the core component of a broader Rough Set Year in India initiative, RSIndia09. The 56 revised full papers presented together with 6 invited papers and a report on the Rough Set Year in .
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Classification is an important Data Mining Technique with broad applications in every walk of life. It is termed as classifying each item in a set of data into one of predefined set of classes or groups. The present study compares the performance evaluation of Naïve Bayes, Random Forest, k Star, Multilayer Preceptron, j48 classification algorithm and Rough Set Theory. The paper presents the experimental results about classification accuracy and explores that the accuracy of Rough Set Theory is improved than other
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Proceedings of World Academy of …
AbstractKnowledge Discovery in Databases (KDD) has evolved into an important and active area of research because of theoretical challenges and practical applications associated with the problem of discovering (or extracting) interesting and previously unknown knowledge .
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International Journal of Computer Applications
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