The preprocessing of data is an important task in rough set theory as well as in Entropy. The discretization of data as part of the preprocessing of data is a very influential process. Is there a connection between the segmentation of the data histogram and data discretization? The authors propose a novel data segmentation technique based on a histogram with regard to the quality of a data discretization. The significance of a cut’s position has been researched on several groups of histograms. A data set reduct was observed with respect to the histogram type. Connections between the data histograms and cuts, reduct and the classification rules have been researched. The result is that the reduct attributes have a more irregular histogram than attributes out of the reduct. The following discretization algorithms were used: the entropy algorithm and the Maximal Discernibility algorithm developed in rough set theory. This article presents the Cuts Selection Method based on histogram segmentation, reduct of data and MD algorithm of discretization. An application on the selected database shows that the benefits of a selection of cuts relies on histogram segmentation. The results of the classification were compared with the results of the Naïve Bayes algorithm.

The main goal of this paper is to investigate the performance of rule sets obtained by different techniques based on the rough set theory. The performance of a rule set is defined as its ability to classify a set of objects into predefined classes. The set of rules contains the if then form rules. The set of objects is represented by flat table (table, organized data) where each object is represented by its attributes. To generate the rule sets we have used Rosetta software system. The performance of rule sets is measured by employment of the confusion matrix.

Rough sets theory (Pawlak 1980s) proved to be an excellent mathematical tool for the task of automated extraction of If –Then rule sets from table-organized data. In this paper, we employ an approach based on the relation of indiscernibility and Rough sets theory in comparison to the method based on pure classification. We have used a well-known ROSETTA software system. The main goal of this work is to compare rule set generated by ROSETTA and rule set generated by method based on pure classification. Comparison is conducted on real–life database from domain of medicine including recently discovered protein hormone Leptin.

Data mining techniques and their applications are widely recognized as powerful tools in various domains. Educational data mining is an emerging discipline, concerned with developing methods for exploring the unique types of data that come from the educational context. In the domain of education, there are varieties of data of different types collected during the educational process. The main question is: Is it possible to process the collected data with the data mining system and what are main advantages of data mining and e-learning interaction? In this paper, we present an insight into the possible interaction between course management system and data mining techniques. The main goal of this work is to investigate some data mining techniques in order to deliver most appropriate learning object to the learner. In this paper, visualization as a data mining technique is investigated. For this purpose, free data mining tool Weka was used.

One of the three goals of this work is to research automated knowledge acquisition process from table-organized data by using the Rough set theory. This theory proved to be an excellent mathematical tool for the task of automated extraction of rule sets from table-organized data. The second goal is to employ a system for automated theorem proving -LogPro, based on ordered linear resolution with marked literals. This approach has shown better performances that the one based on linear resolution with selection function for definite clauses and negation as failure, e.g. Prolog. The rule sets produced by rough sets technique, in form of Prolog-like clauses are generated from real life medical table-organized data including 36 parameters, one of them being protein hormone Leptin. The third goal of this work is to get new insight into phenomena of Leptin levels while interplaying with other risk factors in obesity, especially with Trunk Fat. This is done by employing generated rule sets in LogPro.