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Data Mining
Slezák, Milan ; Hynčica, Ondřej (referee) ; Honzík, Petr (advisor)
The thesis is focused on an introduction of data mining. Data mining is focused on finding of a hidden data correlation. Interest in this area is dated back to the 60th the 20th century. Data analysis was first used in marketing. However, later it expanded to more areas, and some of its options are still unused. One of methodologies is useful used for creating of this process. Methodology offers a concise guide on how you can create a data mining procedure. The data mining analysis contains a wide range of algorithms for data modification. The interest in data mining causes that number of data mining software is increasing. This thesis contains overviews some of this programs, some examples and assessment.
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Classification of microsleep by means of analysis EEG signal
Ronzhina, Marina ; Smital, Lukáš (referee) ; Čmiel, Vratislav (advisor)
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.
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Possibilities of using multi - dimensional statistical analyses methods when evaluating reliability of distribution networks
Geschwinder, Lukáš ; Skala, Petr (referee) ; Blažek, Vladimír (advisor)
The aim of this study is evaluation of using multi-dimensional statistical analyses methods as a tool for simulations of reliability of distribution network. Prefered methods are a cluster analysis (CLU) and a principal component analysis (PCA). CLU is used for a division of objects on the basis of their signs and a calculation of the distance between objects into groups whose characteristics should be similar. The readout can reveal a secret structure in data. PCA is used for a location of a structure in signs of multi-dimensional matrix data. Signs present separate quantities describing the given object. PCA uses a dissolution of a primary matrix data to structural and noise matrix data. It concerns the transformation of primary matrix data into new grid system of principal components. New conversion data are called a score. Principal components generating orthogonal system of new position. Distribution network from the aspect of reliability can be characterized by a number of new statistical quantities. Reliability indicators might be: interruption numbers, interruption time. Integral reliability indicators might be: system average interruption frequency index (SAIFI) and system average interruption duration index (SAIDI). In conclusion, there is a comparison of performed SAIFI simulation according to negatively binomial division and provided values from a distribution company. It is performed a test at description of sign dependences and outlet divisions.
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Analysis of AVG signals
Musil, Václav ; Sekora, Jiří (referee) ; Rozman, Jiří (advisor)
The presented thesis discusses the basic analysis methods of arteriovelocitograms. The core of this work rests in classification of signals and contribution to possibilities of noninvasive diagnostic methods for evaluation patients with peripheral ischemic occlusive arterial disease. The classification employs multivariate statistical methods and principles of neural networks. The data processing works with an angiographic verified set of arteriovelocitogram dates. The digital subtraction angiography classified them into 3 separable classes in dependence on degree of vascular stenosis. Classification AVG signals are represented in the program by the 6 parameters that are measured on 3 different places on each patient’s leg. Evaluation of disease appeared to be a comprehensive approach at signals acquired from whole patient’s leg. The sensitivity of clustering method compared with angiography is between 82.75 % and 90.90 %, specificity between 80.66 % and 88.88 %. Using neural networks sensitivity is in range of 79.06 % and 96.87 %, specificity is in range of 73.07 % and 91.30 %.
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Unsupervised learning
Kantor, Jan ; Sáblík, Václav (referee) ; Honzík, Petr (advisor)
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.
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Cluster analysis in biosignal processing
Kalous, Stanislav ; Archalous, Tomáš (referee) ; Kolářová, Jana (advisor)
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.
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Texture analysis of tumor in lung CT data
Šalplachta, J.
The aim of this work is the revelation of the possibility of the use of texture analysis methods to detection and segmentation tumor tissue in patient’s lungs and classification viable areas of tumor tissue. The main assumption includes the possibility that there are differences of textural properties between tumor and surrounding tissues and changes of these features during development and treatment of this disease. The work deals with the creation of vector of texture features which is composed of some methods of texture analysis and then processed by methods of cluster analysis in programming environment Matlab®.
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Voting analysis of the Chamber of Deputies
Zubatý, Radek ; Vrabec, Michal (advisor) ; Řezanková, Hana (referee)
Objective of this thesis is cluster analysis of political parties and individual politicians in Chamber of Deputies of the Parliament of the Czech Republic. In theoretical part, legislative process in Czech Republic is explained and political situation in Chamber of Deputies is described. Also cluster analysis theory is explained. In practical part, firstly, cluster analysis of political parties was performed, than of individual members of parliament. The furthest neighbor and Ward's methods were used in this part. On the basis of discovered facts, it's possible to confirm compactness of coalition and also of conservative opposition. In specific political parties, as most united behave TOP 09 members and least ČSSD members of parliament.
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Analysis of marriage rate, divorce rate and live births outside marriage
Birčáková, Barbora ; Arltová, Markéta (advisor) ; Löster, Tomáš (referee)
The main goal of the thesis is to analyze the basic indicators of marriage rate, divorce rate and the proportion of live births outside marriage. The first part is focused on the evaluation of the past and present development of the selected indicators. The thesis also includes a prediction of the future development of these indicators by using the Box-Jenkins methodology. The last part is dedicated to an international comparison of marriage, divorce and non-marital fertility indicators in the selected countries of the European Union. Moreover, the last part also includes a cluster analysis, where countries are divided into homogeneous groups according to the selected indicators.
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Cluster analysis as a tool for classification of objects
Budilová, Šárka ; Löster, Tomáš (advisor) ; Šulc, Zdeněk (referee)
Cluster analysis is a popular method of multivariate statistics. Based on mutual similarities between objects this method is able to classify and divide objects into several groups or clusters. The results of the clustering can be different by using different methods, measures of distance and procedures. The main aim of this thesis is to compare the results of several methods of cluster analysis with the known classification of classes from the original data file. In total, there are 15 data files, which were analyzed and each of them contained known information about the right allocation of objects in groups. The success of clustering of each method was calculated by comparing the known classification of classes and resulted clusters. In addition to the comparison of individual methods of cluster analysis was compared the impact of standardization and correlation to the success of each method. To reflect the distance betweeen the objects within each clusters, squared Euclidean distance was used. The results of this thesis point out that better success of clustering were achieved in the case of correlated variables in data file. The succes of clustering was higher about 2 percent points than in the case when correlated variables were deleted from data set. The methods divided 69,8 % objects before standardization and 70,8 % objects after standardization. The results also show a large importance of standardization in the case of Ward´s method. After standardization this method rank the most objects into correct classification classes and were more succesful, about nine percent points. In the case of correlated variables is the succes of the method 76,4 %. Standardization positively influences also centroid method and the method of farthest neighbour. Median method, nearest neighbour method and the method of average linkage achieve higher success of clustering in the case of original, nonstandardized variables (uneven variables).
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