### Перегляд за автором "Sitnikov, D."

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Публікація A generalized algebraic approach to finding rough set approximations and generating logic rules(WIT Transactions on Information and Communication Technologies, 2007) Sitnikov, D.; Ryabov, O.; Titova, O.; Romanenko, O.Показати більше The rough set concept is a relatively new mathematical approach to vagueness and uncertainty in data. The rough set theory is a well-understood formal framework for building data mining models in the form of logic rules, on the basis of which it is possible to issue predictions that allow the classification of new cases. The indiscernibility relation and approximations based on this relation form the mathematical basis of the rough set theory. The classical topological definitions of rough approximations are based on this relation. Unlike the classical approaches it is possible to define rough approximations in an algebraic way. This paper represents a generalization of the algebraic approach suggested by the authors earlier. We use a set of discrete characteristic functions taking on values from finite sets (not necessarily Boolean values) and operations on them including comparison and Boolean operations, which we call the approximation language. We use the terms \“exact upper approximation” and \“exact lower approximation” to stress the fact that there can exist a variety of approximations but it is always possible to select the approximations that cannot be improved in the terms of the approximation language. We consider the process of generating logic rules based on the exact approximations in the case of arbitrary discrete characteristic functions taking on values from finite sets. Logic rules are naturally obtained from predicate formulae for the exact approximations. The introduced approach allows the generation of logic rules quickly and efficiently since only comparison operations with discrete values and Boolean operations with binary values are used to produce logic formulae.Показати більше Публікація A method for association rule quality evaluation based on information theory(WIT Transactions on Information and Communication Technologies, 2006) Sitnikov, D.; Titova, O.; Ryabov, O.Показати більше The concept of patterns representing functional, logical and other dependencies in data lies in the basis of the Data Mining technology. One of the wide spread forms for representing discovered knowledge patterns is association rules. A method for evaluating an association rule from the viewpoint of information theory has been suggested, which allows us to calculate a generalized characteristic of associations (based on mutual information) with the help of the well known association rule parameters: Support, Confidence and Improvement. Using such a characteristic of associations complements the traditional association parameters and allows setting a linear order on the set of associations, which is useful for evaluating and filtering obtained dependencies. Besides we have carried out analysis of the dependence of the association rule self-descriptiveness on the standard parameters.Показати більше Публікація A method for building desktop software automated update systems(WIT Transactions on information and communication technologies, 2013) Sitnikov, D.; Sitnikov, A.; Ryabov, O.Показати більше One of the problems in modern software development is the problem of introducing changes and fixes to the deployed software products in an automated fashion thus not requiring the end user to download the redundant product modules that had not been changed and performing manually the uninstall/install/check sequence. In this paper a method for building an automated update system has been suggested. The automated update system guarantees that the end user receives the latest changes and fixes to the product thus maintaining smooth experience from the software product. In this paper one of the methods for implementation of an automatic software update system for desktop applications is considered.Показати більше Публікація A method for finding minimal sets of features adequately describing discrete information objects(WIT Transactions on Information and Communication Technologies, 2009) Sitnikov, D.; Titova, O.; Romanenko, O.; Ryabov, O.Показати більше One of the classical Data Mining problems is the problem of classifying new objects on the basis of available information when the information associated with these objects does not allow identifying them unambiguously as elements of some set. In such cases using rough sets theory is often an effective solution. This theory operates with such concepts as \“indiscernible” elements and relations. A rough set is characterized by lower and upper approximations for finding which the authors earlier suggested an original algebraic method. The given method uses only logic operations, which makes the process of searching logic rules very quick and efficient. The upper and lower approximations of a rough set allow describing elements of this set as completely as it is possible from the viewpoint of available information. In this connection it seems interesting and important to find irreducible sets of features describing a rough set with the same \“precision” as with the help of a full set of features (so called reducts). This problem is quite difficult and complicated and at present it does not have good solutions. Our paper continues research carried out by the authors earlier and we suggest a method for finding reducts based on eliminating non-salient features in the reverse order of their importance. The suggested procedure allows us to avoid exhaustive searching by extracting a predefined number of most significant reducts. In this paper we consider arbitrary features taking on their values from finite sets. Keywords: rough set, low approximation, upper approximation, boundary region, reduct.Показати більше Публікація An algebraic approach to defining rough set approximations and generating logic rules(WIT Transactions on Information and Communication Technologies, 2004) Sitnikov, D.; Ryabov, O.Показати більше The rough set concept is a relatively new mathematical approach to vagueness and uncertainty in data. The rough set theory is a well-understood formal framework for building data mining models in the form of logic rules, on the basis of which it is possible to issue predictions that allow classifying new cases. The indiscernibility relation and approximations based on this relation form the mathematical basis of the rough set theory. The classical topological definitions of rough approximations are based on the indiscernibility relation. Unlike the classical approaches, in this paper we define rough approximations in an algebraic way. We use a set of predicates and predicate operations, which we call the approximation language. We introduce the terms "exact upper approximation" and "exact lower approximation" to stress the fact that there can exist a variety of approximations but it is always possible to select the approximations that cannot be improved in the terms of the approximation language. These new definitions are compared to the classical ones (which use an equivalence relation) and are shown to be more general in the sense that the classical definitions can be deduced from them if we put some restrictions on our model. The process of generating logic rules based on the exact approximations has also been considered. Logic rules are naturally obtained from predicate formulae for the exact approximations. The introduced approach allows generating logic rules quickly and efficiently since only Boolean operations with binary strings are used to produce logic formulae.Показати більше Публікація An approach to finding reduced sets of information features describing discrete objects based on rough sets theory(WIT Transactions on Information and Communication Technologies, 2008) Sitnikov, D.; Titova, O.; Romanenko, O.; Ryabov, O.Показати більше Modern Data Mining methods allow discovering non-trivial dependencies in large information arrays. Since these methods are used for processing and analysis of huge information volumes, reducing the number of features necessary for describing a discrete object is one of the most important problems. One of the classical problems in intelligent data analysis is the problem of classifying new objects based on some a-priori information. This information might not allow us to exactly classify an object as one belonging to a certain set. In such cases using rough sets theory may be an effective solution as this theory operates with the concept of \“indiscernible” elements and ambiguous information. In this paper we introduce a concept of a local reduct as a reduced set of features allowing us to describe a particular subset of the original set with the same precision as with the help of the full set of features. A method has been suggested which allows finding reduced sets of features adequately describing a rough set without losing necessary information (so-called reducts), and also assessing the importance of each feature. The suggested method is based on the algebraic approach to finding rough set approximations developed by the authors earlier. The main idea of the developed approach is as follows: if the algebraic approximations of a rough set do not change substantially in the process of excluding features the resulting reduced set of features can be used instead of the original full set. Also the greater changes eliminating a particular feature causes in the approximations, the more important this feature is. Keywords: data mining, rough sets, rough approximations, reduct.Показати більше Публікація Defining logic structures in functional spaces(WIT Transactions on Information and Communication Technologies, 2007) Sitnikov, D.; Matski, N.; Ryabov, O.Показати більше Many practical problems of geometric information representation and pattern recognition require specific methods and tools for describing complex functions and geometric objects in a way that allows us to use such descriptions for making effective mathematical transformations and logic inferences. Since in real world situations we rarely encounter ideal objects that can be described with the help of one elementary function it is important to develop methods and models for integrating geometric information. There are some original approaches to describing complicated geometric structures, one of which is a method based on so-called R-functions. These functions allow complicated geometric objects to be described analytically, which gives an opportunity to operate with complex objects with the help of a single real function. The concept of R-functions is quite simple but the range of practical applications of this theory is very broad. In this paper we have suggested a generalization of R-functions, which we call Rp-functions. Any R-function can be associated with a Boolean function and it is always possible to construct an R-function corresponding to a given Boolean one. Similarly, any Rp-function can be associated with a finite predicate and it is always possible to construct an Rp-function corresponding to a given predicate. If R-functions allow us to use Boolean logic for describing complex objects, Rp-functions provide a possibility of using predicate logic, which is more rich and general than Boolean one. Like R-functions, Rp-functions allow the use of knowledge on what logic rules have been used for constructing a complex object to build a function which is positive inside the given domain and negative outside it. Rp-functions also seem to be interesting by themselves as they allow discovery of logic properties of quite a large class of real functions.Показати більше