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Discretization of data using Boolean transformations and information theory based evaluation criteria

Jankowski C., Reda D., Mańkowski M., Borowik G.

Bulletin of the Polish Academy of Sciences. Technical Sciences

2015

Vol. 63, nr 4

923--932

Discretization is one of the most important parts of decision table preprocessing. Transforming continuous values of attributes into discrete intervals influences further analysis using data mining methods. In particular, the accuracy of generated predictions is highly dependent on the quality of discretization. The paper contains a description of three new heuristic algorithms for discretization of numeric data, based on Boolean reasoning. Additionally, an entropy-based evaluation of discretization is introduced to compare the results of the proposed algorithms with the results of leading university software for data analysis. Considering the discretization as a data compression method, the average compression ratio achieved for databases examined in the paper is 8.02 while maintaining the consistency of databases at 100%.

Features Reduction Using Logic Minimization Techniques

Borowik G., Łuba T., Zydek D.

International Journal of Electronics and Telecommunications

2012

Vol, 58, No. 1

71-76

This paper is dedicated to two seemingly different problems. The first one concerns information theory and the second one is connected to logic synthesis methods. The reason why these issues are considered together is the important task of the efficient representation of data in information systems and as well as in logic systems. An efficient algorithm to solve the task of attributes/arguments reduction is presented.