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Artificial Intelligence Methods in geoinformatics
Voženílek, V. ; Dvorský, J. ; Húsek, Dušan
The book "Artificial Intelligence Methods in geoinformatics", (editors: Voženílek, V. - Dvorsky, J. - Husek, D) issued by the University of Palacky in Olomouc (in 2011. 184 pp. ISBN 978-80-244-2945-8) is the final outcome of the project GACR 205/09/1079 - "Artificial intelligence methods in GIS." The aim of the book was to summarize the results of research on the application of artificial intelligence methods for processing, visualization and interpretation of spatial information in the area of geographic information systems. The project was a joint research activity of experts from various research fields of Palacky University in Olomouc, VSB-Technical University of Ostrava, and Institute of Computer Science, Academy of Sciences Czech Republic targeted to the integration of methods used in solving problems of artificial intelligence and methods of processing spatial data, whose integration is essential for the development of new modern research methods and technologies.
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Cluster analysis of large data sets: new procedures based on the method k-means
Žambochová, Marta ; Řezanková, Hana (advisor) ; Húsek, Dušan (referee) ; Antoch, Jaromír (referee)
Abstract Cluster analysis has become one of the main tools used in extracting knowledge from data, which is known as data mining. In this area of data analysis, data of large dimensions are often processed, both in the number of objects and in the number of variables, which characterize the objects. Many methods for data clustering have been developed. One of the most widely used is a k-means method, which is suitable for clustering data sets containing large number of objects. It is based on finding the best clustering in relation to the initial distribution of objects into clusters and subsequent step-by-step redistribution of objects belonging to the clusters by the optimization function. The aim of this Ph.D. thesis was a comparison of selected variants of existing k-means methods, detailed characterization of their positive and negative characte- ristics, new alternatives of this method and experimental comparisons with existing approaches. These objectives were met. I focused on modifications of the k-means method for clustering of large number of objects in my work, specifically on the algorithms BIRCH k-means, filtering, k-means++ and two-phases. I watched the time complexity of algorithms, the effect of initialization distribution and outliers, the validity of the resulting clusters. Two real data files and some generated data sets were used. The common and different features of method, which are under investigation, are summarized at the end of the work. The main aim and benefit of the work is to devise my modifications, solving the bottlenecks of the basic procedure and of the existing variants, their programming and verification. Some modifications brought accelerate the processing. The application of the main ideas of algorithm k-means++ brought to other variants of k-means method better results of clustering. The most significant of the proposed changes is a modification of the filtering algorithm, which brings an entirely new feature of the algorithm, which is the detection of outliers. The accompanying CD is enclosed. It includes the source code of programs written in MATLAB development environment. Programs were created specifically for the purpose of this work and are intended for experimental use. The CD also contains the data files used for various experiments.
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Classification Methods for Brain-Computer Interface
Bobrov, P. ; Frolov, A. A. ; Húsek, Dušan
The performance of four classifiers for Brain Computer Interface (BCI) systems based on multichannel EEG recordings is tested in this work. The classifiers are designed to distinguish EEG patterns corresponding to performance of several mental tasks. It is shown that relatively simple classifiers based on the Bayesian approach are comparable in classification accuracy with more sophisticated classifiers based on Common Spatial Patterns and Common Tensor Discriminant Analysis
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New Developments in Fuzzy Cluster Analysis
Řezanková, H. ; Húsek, Dušan
The paper deals with a special class of cluster analysis methods where a membership degree is calculated for each object and each cluster. These methods are investigated under the name fuzzy cluster analysis. We present some emerging topics in this area, such as relation fuzzy clustering, soft clusters ensembles, similarity of fuzzy clusters, visualization of clustering results, simultaneous clustering and feature discrimination, and techniques for cluster number determination. Some tasks are illustrated by clustering of binary variables.
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Special Issue on Hybrid Intelligent Systems 2007
Abraham, A. ; Húsek, Dušan ; Snášel, V.
Special Issue on Hybrid Intelligent Systems 2007. Neural Network World. Vol. 17, No. 6 (2007), p.505-688 The issue contains papers prepared specially for this issue by authors of some best evaluated papers presented on HIS'07) at Kaiserslautern, Germany, during September 17-19, 2007. The Current research interests in HIS and covered in this issue focus on integration of the different computing paradigms such as fuzzy logic, euro-computation, evolutionary computation, probabilistic computing, intelligent agents, machine learning, and other intelligent computing frameworks. There is also a growing interest in the role of sensors, their integration and evaluation in such frameworks. The phenomenal growth of hybrid intelligent systems and related topics has obliged.
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Special Issue on the 18th International Conference on Artificial Neural Networks
Húsek, Dušan ; Neruda, Roman ; Koutník, J.
Special Issue on the 18th International Conference on Artificial Neural Networks. Neural Network World. Vol. 19, No. 5 (2009). The issue contains papers prepared specially for this issue by authors of some best evaluated papers presented on ICANGA 2008 conference. Covered are mainly following topics: Mathematical Theory of Neurocomputing, Computational Neuroscience, Connectionist Cognitive Science, Neuroinformatics, Image Processing, Signal and Time Series Processing, Reinforcement Learning, Binary Factor Analysis, Principal Component Analysis, Self-organization, Neural Network Hardware.
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