This diploma thesis deals with cluster analysis for long-term electrocardiograms (ECG) clustering. The linear filtration is used for ECG preprocessing. The ECG sign segmenting in single heart cycles is based on the detection QRS complex and consequently to an application of dynamic time warping algorithms. To an application of all these mentioned processes and to results interpretation, a program called Cluster analysis has been created in the Matlab background. The results of this diploma thesis confirm that cluster analysis is able to distinguish cardiac arrhythmias which are typical with their shape distinctness of normal heart cycles.

The purpose of this work has been to describe some techniques which are normally used for cluster data analysis process of unsupervised learning. The thesis consists of two parts. The first part of thesis has been focused on some algorithms theory describing advantages and disadvantages of each discussed method and validation of clusters quality. There are many ways how to estimate and compute clustering quality based on internal and external knowledge which is mentioned in this part. A good technique of clustering quality validation is one of the most important parts in cluster analysis. The second part of thesis deals with implementation of different clustering techniques and programs on real datasets and their comparison with true dataset partitioning and published related work.

This master thesis deals with detection of microsleep on the basis of the changes in power spectrum of EEG signal. The results of time-frequency analysis are input values for the classifikation. Proposed classification method uses fuzzy logic. Four classifiers were designed, which are based on a fuzzy inference systems, that are differ in rule base. The results of fuzzy clustering are used for the design of rule premises membership functions. The two classifiers microsleep detection use only alpha band of the EEG signal’s spectrogram then allows the detection of the relaxation state of a person. Unlike to first and second classifiers, the third classifier is supplemented with rules for the delta band, which makes it possible to distinguish the 3 states: vigilance, relaxation and somnolence. The fourth classifier inference system includes the rules for the whole spectrum band. The method was implemented by computer. The program with a graphical user interface was created.

This master’s thesis is engaged in usage of cluster analysis for ECG signal to separate normal QRS complexes from abnormal ones. For this, it is used two algorithms created in professional computing interface MATLAB. The outputs from this master’s thesis are dendrograms, which divide QRS complexes into abnormal and normal clusters, and Pearson correlation coefficients.

The aim of this work is to study and describe the principles and method of delineation of ECG signals. Learn and describe about method of cluster analysis. In this work was created and described three different methods of delineations of ECG signals. Created algorithms were tested on complete CSE database. With cluster analysis were combine created methods. The obtained results from realized methods and combined method were compared with others known methods. At the end of this work is evaluate efficiency of detection of combined method.

Changes in chromosome number and structure may cause serious diseases. Cytogenetic tests leadin to set of karyotype are done for detecting these abnormalities. Chromosomes are visualised with proper methods and karyotype is made up most often. Manual karyotyping is time-consuming and expensive task. Because of this, researchers have been developing automated karyotyping systems. Karyotyping systems classify chromosomes into classes based on their characteristic features. Overlapping and bent chromosomes are limitations for automatic classification since they ocur at almost every mitosis. Accuracy and reliability of karyotyping systems still depend on the human intervention. Overcoming of these problems and development of fully automated system is the aim of modern approaches.

Tato disertační práce se zaměřuje na problematiku investování a podporu rozhodování pomocí moderních metod. Zejména pokud jde o analýzu, hodnocení a výběr tzv. brownfieldů pro jejich redevelopment (revitalizaci). Cílem této práce je navrhnout univerzální metodu, která usnadní rozhodovací proces. Proces rozhodování je v praxi komplikován též velkým počet relevantních parametrů ovlivňujících konečné rozhodnutí. Navržená metoda je založena na využití fuzzy logiky, modelování, statistické analýzy, shlukové analýzy, teorie grafů a na sofistikovaných metodách sběru a zpracování informací. Nová metoda umožňuje zefektivnit proces analýzy a porovnávání alternativních investic a přesněji zpracovat velký objem informací. Ve výsledku tak bude zmenšen počet prvků množiny nejvhodnějších alternativních investic na základě hierarchie parametrů stanovených investorem.

This work deals with issues in data streams mining which nowadays is a very dynamic area in information technology. The thesis describes the general principles of data mining. There are also the principles of data mining in the data streams. Special attention is given to the implemented algorithm CluStream. In the practical part the data stream processing solution was designed and implemented by the MSSQL technology using the above algorithm. The functionality of the algorithm was verified using own data stream generator.

The master' s thesis deals with cluster data analysis. There are explained basic concepts and methods from this domain. Result of the thesis is Cluster analysis tool, in which are implemented methods K-Medoids and DBSCAN. Adjusted results on real data are compared with programs Rapid Miner and SAS Enterprise Miner.

The thesis describe issue of segmentation and classification of Petri dishes colored images. There is proposed a segmentation method that extracts positions of cells from the image. Another techniques focues on classification to groups according to the parameters obtained from images. Reliability of each optimalized algorithm is tested on database containing 250 colored images received from company BioVendor Instruents a.s.