POSSIBILITIES OF PERFORMING BANKRUPTCY DATA ANALYSIS USING TIME SERIES CLUSTERING

Prediction of corporate bankruptcy is a study topic of great interest. Under the conditions of the modern free market, early diagnostics of unfavourable development trends of company’s activity or bankruptcy becomes a matter of great importance. There is no general method which would allow one to forecast unfavourable consequence with a high confidence degree. This paper focuses on the analysis of the approaches that can be used to perform an early bankruptcy diagnostics- in previous research multivariate discriminant analysis (MDA), neural network based approach and rule extraction method have been examined. Lately, time series clustering approach has become popular and its feasibility for bankruptcy data analysis is being investigated. Experiments carried out validate the use of such methods in the given class of tasks. As a novelty, an attempt to apply time series clustering method to the analysis of bankruptcy data is made

Slēpto neironu loma tiešās izplatības tīklos

Parādīta slēpto neironu loma tiešās izplatības mākslīgajos neironu tīklos. Slēpto neironu skaita izteiksme parasti tiek noteikta katrā atsevišķā gadījumā empīriski. Aprakstītas metodikas slēpto neironu skaita noteikšanai

IMPACT OF PARAMETERS CHARACTERIZING CLUSTERING ON DATA ANALYSIS RESULTS

Clustering algorithms are used to group some given objects defined by a set of numerical properties in such a way that the objects within a group are more similar than the objects in different groups. All clustering algorithms have common parameters the choice of which characterizes the effectiveness of clustering. The most important parameters characterizing clustering are: metrics (the distance between cluster elements and cluster centre), number of clusters k and cluster validity criteria. The goal of the paper – to perform the evaluation of the validity of metrics’ choice, to describe the change with respect to the number of clusters for experimental data purposes and to evaluate the credibility of clustering results. As an input data the table describing the rating of Latvian state higher educational institutions for year 2011 has been used and the goal of the experiment was to show, how by using the clustering methods it is possible to analyze the mentioned data in an alternative way

APPLICATION OF CLUSTERING METHOD IN THE RBF NEURAL NETWORKS

This paper describes one of classification algorithms, cluster analysis, that plays a significant role in the implementation of learning algorithm as applied to RBF-type artificial mural networks. The mathematical description of the K-means clustering algorithm is given and its implementation is demonstrated by experiment

ANALYSIS OF THE SIMULATED ANNEALING METHOD IN CLASSIC BOLTZMANN MACHINES

The paper analyses a model of a neural net proposed by Hinton et al (1985). They have added noise to a Hopfield net and have called it Boltzmann machine (BM) drawing an analogy with the behaviour of physical systems with noises. The concept of simulated annealing is analysed. The experiment aimed at testing the state of thermal equilibrium for a Boltzmann net with three neurons, specified threshold values and weights at two different temperatures, T=1 and T=0,25, is described

THE POSSIBILITIES OF CLUSTERING LEARNING METHODS IN STUDENT EDUCATION

Many educational courses operate with models that were previously available only in mathematics or other learning disciplines. As a possible solution, there could be the use of package IBM SPSS Statistics and Modeler in realization of different algorithms for IT studies. Series of research were carried out in order to demonstrate the suitability of the IBM SPSS for the purpose of visualization of various simulation models of some data mining disciplines – particularly cluster analysis. Students are very interested in modern data mining methods, such as artificial neural networks, fuzzy logic and clustering. Clustering methods are often undeservedly forgotten, although the implementation of their algorithms is relatively simple and can be implemented even for students. In the research part of the study the modelling capabilities in data mining studies, clustering algorithms and real examples are demonstrated

APPLICATION POSSIBILITIES OF ASSOCIATION RULES IN STATISTICAL DATA ANALYSIS

This paper studies one of intelligent data processing methods: using association rules for data analysis. The method of association rule obtaining what was initially developed to analyse consumer’s basket has turned to be a good tool for other tasks too. The method helps search and find regularities of the form X Y in different kinds of data. Nowadays this method is widely applied in the tasks of large scale database processing and analysing. As a result, methods of association rule construction occupy their place among the basic methods of intelligent data processing. The paper consists of two parts: theoretical and experimental. The theoretical part examines the mathematical aspects of association rule construction in detail and describes basic concepts and algorithm application possibilities. The experimental part presents implementation results and analysis of experiments. Conclusions have been drawn concerning the efficiency of association rules’ application in search of regularities. Even though the association rules mining method is among the fundamental data processing methods, in Latvia this method is not widely used, therefore, the article under consideration reveals the potential possibilities of the association rule mining in the analysis of statistical data

SIMULATION MODELLING POSSIBILITIES IN TEACHING ECONOMIC PROCESSES

For the purpose of simulation models visualization of various economic disciplines, it is appropriate to use specialized programs that allow to characterize the nature of a particular model, but also make it possible to carry out a simulation model based on various parameters. This article substantiates the usefulness of introduction the simulation models at the initial research process, when simulation models can be imported parallel with analytical relations acquisition. Series of research were carried out in order to demonstrate the suitability of the Matlab Simulink for the purpose of visualization of various simulation models of various economic disciplines. Often, the analytical solution is much simpler than the visual Simulink model, but in the perspective of training purposes, it gives an understanding of the usefulness of such models. In the research part of the study the modelling capabilities in economic studies were demonstrated- adapted models in optimal tax rates computing and equilibrium determination in the competitive market

COMPARISON OF COMPUTER LOCAL BUSES

The author in this work provides insight into computer local buses. Themes which are discussed is breakdown by functional significance, data bus, address bus, control bus, description of SATA, PCI Express, HDMI buses, Intel Matrix Storage Technology, Intel Turbo Memory with User Pinning. Also was made the conclusions

EVOLUTIONARY ALGORITHMS LEARNING METHODS IN STUDENT EDUCATION

Teaching experience shows that during educational process student perceive graphical information better than analytical relationships. As a possible solution, there could be the use of package Matlab in realization of different algorithms for IT studies. Students are very interested in modern data mining methods, such as artificial neural networks, fuzzy logic, clustering and evolution methods. Series of research were carried out in order to demonstrate the suitability of the Matlab for the purpose of visualization of various simulation models of some data mining disciplines – particularly genetic algorithms. Nowadays the possibilities of evolutionary algorithms are widely used in many optimization and classification tasks. There are four paradigms in the world of evolutionary algorithms: evolutionary programming, evolution strategies, genetic algorithms and genetic programming. This paper analyses present-day approaches of genetic algorithms and genetic programming and examines the possibilities of genetic programming that will be used in further research. Genetic algorithm learning methods are often undeservedly forgotten, although the implementation of their algorithms is relatively strong and can be implemented even for students. In the research part of the study the modelling capabilities in data mining studies were demonstrated based on genetic algorithms and real examples. We assume that students already have prior knowledge of genetic algorithms.