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DNA Microarrays Data Analysis
Hebelka, Tomáš ; Jaša, Petr (referee) ; Burgetová, Ivana (advisor)
This work concerns with data analysis of DNA microarrays by using cluster analysis. It explains biological terms - gene expression and DNA microarray. Next, it contains mathematical and informatical description of clustering methods and describes a way to apply these methods to microarrays data. Next, the work contains implementation's detail of clustering methods k-means, DBSCAN and introduces an original clustering algorithm Strom++. Then, description of implementation and application manual follow. Finally, accomplished results are evaluated.
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Knowledge Discovery in Multimedia Databases
Málik, Peter ; Bartík, Vladimír (referee) ; Chmelař, Petr (advisor)
This master"s thesis deals with the knowledge discovery in multimedia databases. It contains general principles of knowledge discovery in databases, especially methods of cluster analysis used for data mining in large and multidimensional databases are described here. The next chapter contains introduction to multimedia databases, focusing on the extraction of low level features from images and video data. The practical part is then an implementation of the methods BIRCH, DBSCAN and k-means for cluster analysis. Final part is dedicated to experiments above TRECVid 2008 dataset and description of achievements.
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Image Database Query by Example
Dobrotka, Matúš ; Hradiš, Michal (referee) ; Veľas, Martin (advisor)
This thesis deals with content-based image retrieval. The objective of the thesis is to develop an application, which will compare different approaches of image retrieval. First basic approach consists of keypoints detection, local features extraction and creating a visual vocabulary by clustering algorithm - k-means. Using this visual vocabulary is computed histogram of occurrence count of visual words - Bag of Words (BoW), which globally represents an image. After applying an appropriate metrics, it follows finding similar images. Second approach uses deep convolutional neural networks (DCNN) to extract feature vectors. These vectors are used to create a visual vocabulary, which is used to calculate BoW. Next procedure is then similar to the first approach. Third approach uses extracted vectors from DCNN as BoW vectors. It is followed by applying an appropriate metrics and finding similar images. The conclusion describes mentioned approaches, experiments and the final evaluation.
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Clustering of Biological Sequences
Kubiš, Radim ; Burgetová, Ivana (referee) ; Martínek, Tomáš (advisor)
One of the main reasons for protein clustering is prediction of structure, function and evolution. Many of current tools have disadvantage of high computational complexity due to all-to-all sequence alignment. If any tool works faster, it does not reach accuracy as other tools. Further disadvantage is processing on higher rate of similarity but homologous proteins can be similar with less identity. The process of clustering often ends when reach the condition which does not reflect sufficient quality of clusters. Master's thesis describes the design and implementation of new tool for clustering of protein sequences. New tool should not be computationally demanding but it should preserve required accuracy and produce better clusters. The thesis also describes testing of designed tool, evaluation of results and possibilities of its further development.
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Using fuzzy clustering in modelling of hardened concrete properties
Haluska, Lukáš ; Vymazal, Tomáš (referee) ; Misák, Petr (advisor)
Compressive strength is one of the most monitored parameter of hardened concrete and it is usually determinated by destructive testing. Non-destructive testing applied on existing constructions uses regression analysis to estimate compressive strength. High variability of outputs and eventually failure of necessary requirements for using regression analysis complicate these methods. The aim of this thesis is to show posibility of using fuzzy clustering in this topic and to compare outputs with classic methods.
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The Application of PSO in Business
Veselý, Filip ; Kaštovský, Petr (referee) ; Dostál, Petr (advisor)
This work deals with two optimization problems, traveling salesman problem and cluster analysis. Solution of these optimization problems are applied on INVEA-TECH company needs. It shortly describes questions of optimization and some optimization techniques. Closely deals with swarm intelligence, strictly speaking particle swarm intelligence. Part of this work is recherché of variants of particle swarm optimization algorithm. The second part describes PSO algorithms solving clustering problem and traveling salesman problem and their implementation in Matlab language.
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Software demo of unsupervised learning
Slezák, Milan ; Sáblík, Václav (referee) ; Honzík, Petr (advisor)
The bachelor's thesis introduces the use of unsupervised learning and presents possibilities of cluster analysis. Software demo of unsupervised learning is a part of this thesis. This program was made as a teaching aid. It consists several input databases with different data distributions on the basis of which it is possible to explain very easily elementary principles of cluster analysis and differences between hierarchical clustering and partitional clustering.
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Text data clustering algorithms
Sedláček, Josef ; Burget, Radim (referee) ; Karásek, Jan (advisor)
The thesis deals with text mining. It describes the theory of text document clustering as well as algorithms used for clustering. This theory serves as a basis for developing an application for clustering text data. The application is developed in Java programming language and contains three methods used for clustering. The user can choose which method will be used for clustering the collection of documents. The implemented methods are K medoids, BiSec K medoids, and SOM (self-organization maps). The application also includes a validation set, which was specially created for the diploma thesis and it is used for testing the algorithms. Finally, the algorithms are compared according to obtained results.
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Application of clustering methods for processing of biomedical data
Rozinek, Michal ; Gazárek, Jiří (referee) ; Havlíček, Martin (advisor)
The goal of this study is to learn about methods in object classification in medicine and find out what are these methods about. Focusing on functionality and reliability of these methods whith datafile from the medicine compartment after making the algorithm in MATLAB. In form of siple tests, put the touch everyone of classification procedure and find out in which they excel and in which they lags. The choice of input data parametres is very important, this will be tested and noted in conclusions.
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The possibilities of application of analytical CRM in the environment of multinational company
Burkova, Alina ; Pour, Jan (advisor) ; Petrlík, Lukáš (referee)
The main goal of this bachelor thesis is showing the main advantages of using certain application, which allows to process big data, for market segmentation and for the following process of defining marketing campaigns. The first part of the thesis is dedicated to definition of the main theoretical terms. They are essential for understanding the main goal of the work. The practical part comes after, and firstly presents concepts of campaign management and market segmentation. The last and the main part is showing the application in action. All the knowledge is used for analysis of the output from the program called BigML.
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