Acta Cybernetica 

About: Acta Cybernetica is an academic journal published by University of Szeged. The journal publishes majorly in the area(s): Tree (data structure) & Computer science. It has an ISSN identifier of 0324-721X. It is also open access. Over the lifetime, 820 publications have been published receiving 6306 citations.

A tourist guide through treewidth

Abstract: A short overview is given of many recent results in algorithmic graph theory that deal with the notions treewidth, and pathwidth. We discuss algorithms that find tree-decompositions, algorithms that use tree-decompositions to solve hard problems efficiently, graph minor theory, and some applications. The paper contains an extensive bibliography.

On the performance of on-line algorithms for partition problems

Abstract: We consider the performance of the greedy algorithm and of on-line algorithms for partition problems in combinatorial optimization. After surveying known resuls we give bounds for matroid and graph partitioning, and discuss the power of non-adaptive adversaries for proving lower bounds.

Nonlinear parameter estimation by global optimization—efficiency and reliability

Abstract: In this paper we first show that the objective function of a least squares type nonlinear parameter estimation problem can be any non- negative real function, and therefore this class of problems corre- sponds to global optimization. Two non-derivative implementations of a global optimization method are presented; with nine standard test functions applied to measure their efficiency. A new nonlinear test problem is then presented for testing the reliability of global op- timization algorithms. This test function has a countable infinity of local minima and only one global minimizer. The region of attraction of the global minimum is of zero measure. The results of efficiency and reliability tests are given.

A fuzzy approach for mining quantitative association rules

Abstract: During the last ten years, data mining, also known as knowledge discovery in databases, has established its position as a prominent and important research area Mining association rules is one of the important research problems in data mining Many algorithms have been proposed to find association rules in databases with binary attributes In this paper, we deal with the problem of mining association rules in databases containing both quantitative and categorical attributes An example of such an association might be ``10% of married people between age 50 and 70 have at least 2 cars'''' We introduce a new definition of quantitative association rules based on fuzzy set theory Using the fuzzy set concept, the discovered rules are more understandable to a human Moreover, fuzzy sets handle numerical values better than existing methods because fuzzy sets soften the effect of sharp boundaries The above example could be rephrased eg 10% of married old people have several cars In this paper we present a new algorithm for mining fuzzy quantitative association rules The algorithm uses new definitions for interesting mea- sures Experimental results show the efficiency of the algorithm for large databases

Fundamental Concepts of Object Oriented Databases.

Abstract: It is claimed that object oriented databases (OODBs) overcome many of the limitations of the relational model. However, the formal foundation of OODB concepts is still an open problem. Even worse, for relational databases a commonly accepted datamodel existed very early on whereas for OODBs the uniication of concepts is missing. The work reported in this paper contains the results of our rst investigations on a formally founded object oriented datamodel (OODM) and is intended to contribute to the development of a uniform mathematical theory of OODBs. A clear distinction between objects and values turns out to be essential in the OODM. Types and Classes are used to structure values and objects repectively. Then the problem of unique object identiication occurs. We show that this problem can be be solved for classes with extents that are completely representable by values. Such classes are called value-representable. Another advantage of the relational approach is the existence of structurally determined generic update operations. We show that this property can be carried over to object-oriented datamodels if classes are value-representable. Moreover, in this case database consistency with respect to implicitly speciied referential and inclusion constraints will be automatically preserved. This result can be generalized with respect to distinguished classes of explicitly stated static constraints. Given some arbitrary method and some integrity constraint there exists a greatest consistent specialization (GCS) that behaves nice in that it is compatible with the conjunction of constraints. We present an algorithm for the GCS construction of user-deened methods and describe the GCSs of generic update operations that are required herein.