Wyniki wyszukiwania - BazTech

Ograniczanie wyników

3 Fundamenta Informaticae

Znaleziono wyników: 3

Liczba wyników na stronie

Wyniki wyszukiwania

Sortuj według:

Ogranicz wyniki do:

Description Languages for Relational Information Granules

Hońko P.

Fundamenta Informaticae

2015

Vol. 137, nr 3

323--340

Information granulation is a powerful tool for data analysis and processing. However, not much attention has been devoted to application of this tool to data stored in a relational structure. This paper extends the notion of information granules to a relational case. Two information systems intended to store relational data are proposed. This study also extends a granule description language to express information granules derived from relational data. The proposed approach enables to analyze a given problem at different levels of granularity of relational data. This can find application in searching for patterns in data mining.

Similarity-Based Classification in Relational Databases

Hońko P.

Fundamenta Informaticae

2010

Vol. 101, nr 3

187-213

In this paper, we introduce a method for measuring similarity of objects of a relational database (relational objects, in short). We also propose and investigate an algorithm SC for classification of relational objects. The task of classification is carried out based on similarity of the objects to predefined classes. An object to be classified is assigned to the class to which it is most similar. A similarity of an object to a class is understood as its similarity to a class representative. Severalmethods for computing the class representative are proposed. We test the algorithm on real and artificial databases. We compare results obtained by the algorithm with those obtained by other algorithms known from the literature. We also present our approach in the context of granular computing.

Learning First-Order Rules: A Rough Set Approach

Stepaniuk J., Hońko P.

Fundamenta Informaticae

2004

Vol. 61, nr 2

139--157

The aim of this paper is to introduce and investigate an algorithm RSRL for finding first-order logic rules. Rough set methodology is used in the process of selecting literals which may be a part of a rule. The criterion of selecting a literal is as follows: only such a literal is selected, which added to the rule makes the rule discerning the most examples which were indiscernible so far.