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Alignment-free Methods for Classification of Metagenomic Data
Vaněčková, Tereza
Metagenomics studies microbial communities by analyzing their genomic content directly sequenced from the environment. In this contribution, alignment-free methods based on word frequency will be introduced. It has been proven, that these methods are effective in processing of short metagenomic sequence reads produced by Next-Generation Sequencing technologies. To evaluate the potential of word frequency based methods, the k-mer analysis was applied on simulated dataset of metagenomic sequence reads with length of 600 nucleotides. Then the data were enrolled for a hierarchical cluster analysis. Results have shown that the proposed method is able to cluster genome fragments of the same taxa.
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Text mining focused on clustering and fuzzy clustering methods
Zubková, Kateřina ; Karpíšek, Zdeněk (referee) ; Žák, Libor (advisor)
This thesis is focused on cluster analysis in the field of text mining and its application to real data. The aim of the thesis is to find suitable categories (clusters) in the transcribed calls recorded in the contact center of Česká pojišťovna a.s. by transferring these textual documents into the vector space using basic text mining methods and the implemented clustering algorithms. From the formal point of view, the thesis contains a description of preprocessing and representation of textual data, a description of several common clustering methods, cluster validation, and the application itself.
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Application of cluster analysis to real data
Onderlička, Tomáš ; Popela, Pavel (referee) ; Žák, Libor (advisor)
This bachelor's thesis deals with finding similar scenarios in the waste management acquired by an optimization tool NERUDA. Cluster analysis, a tool that identifies related objects and classifies them in groups (clusters), is used for this purpose. The aim of this thesis is to review basic algorithms of cluster analysis and to develop a software that implements them. The software is then used to cluster real data from NERUDA which is followed by an assessment of the obtained clusters.
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Numerical methods for classification of metagenomic data
Vaněčková, Tereza ; Sedlář, Karel (referee) ; Škutková, Helena (advisor)
This thesis deals with metagenomics and numerical methods for classification of metagenomic data. Review of alignment-free methods based on nucleotide word frequency is provided as they appear to be effective for processing of metagenomic sequence reads produced by next-generation sequencing technologies. To evaluate these methods, selected features based on k-mer analysis were tested on simulated dataset of metagenomic sequence reads. Then the data in original data space were enrolled for hierarchical clustering and PCA processed data were clustered by K-means algorithm. Analysis was performed for different lengths of nucleotide words and evaluated in terms of classification accuracy.
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Computer Library with Clustering Methods
Riša, Martin ; Homoliak, Ivan (referee) ; Košík, Michal (advisor)
The aim of this work is to create a library with chosen clustering methods, to compare their effectiveness and their properties by testing them on different input data sets. The aim of the testing is to determine efficiency of a method, to determine advantages and disadvantages of a method to cluster general input data or to cluster only data of specific shapes. Stages of development of the library are also documented in the text of this work.
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Network Traffic Analysis Based on Clustering
Černý, Tomáš ; Drahošová, Michaela (referee) ; Bartoš, Václav (advisor)
This thesis focuses on anomaly detection in network traffic using clustering methods. First, basic anomaly detection methods are introduced. The next part describes hierarchical and k-means clustering in detail. Also there are described selected normalization techniques. Part is given to the procedure for detecting anomalies in the context of data mining. Furthermore a few words about implementation of single methods. Finally, clustering methods and normalization techniques are tested and compared.
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Processing of electrochemical metallothionein signals from Brdicka reaction
Dvořáček, Jiří ; Hynek, David (referee) ; Valla, Martin (advisor)
This thesis deals with the Brdička electrochemical reactions and the possibilities of description and processing of metallothionein. The first part deals only marginally, an introduction to the subject, a separate occurrence of this reaction, the discovery of the properties and functions of metallothionein in the human body, describe the possibilities and applications of cluster analysis on the measured electrochemical signal of metallothionein Brdička reactions and detection of the selected peak. The second part is based on requests made program to evaluate response Brdiča working on the basis of two detection signals from within.
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Uniform Marker Field on a Cylinder
Kříž, Radim ; Havel, Jiří (referee) ; Herout, Adam (advisor)
This work presents a new extension for Uniform Marker Field, which is able to detect UMF on the cylinder. First part of the text deals with Augmented reality and focuses on systems using markers. It discusses the actual state-of-the-art systems and its possibilities. After that it focuses more deeply on the marker system Uniform marker field and its grayscale variants. Next part of the work describes properties of the cylinder projected in real space. Important properties for detecting are discussed in detail. Then the proposal and description of detection algorithm is presented. Implementation of algorithm is tested and evaluated on the very end of this thesis.
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Biosignal processing - clusetr analysis
Příhodová, Petra ; Maděránková, Denisa (referee) ; Kolářová, Jana (advisor)
This thesis deals with the problem with cluster analysis and biosignal classification options. The principle of cluster analysis, methods for calculating distances between objects and the standard process in the implementation of clustering are described in the first part. For biosignals processing,it is necessary to get familiar with the primary parameters of these signals in the following sections of thesis, process biosignals and methods for recording of action potentials described. Based on studying different clustering methods is presented a program with the applied method kmedoid in the next section of this thesis. The steps of this program are described in detail and in the end of thesis functionality is tested on a database of signals ÚBMI.
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Analýza regionálních cen nemovitostí ve Spojených státech pomocí vysokodimenzionálního VAR modelu
Krčál, Adam ; Čížek, Ondřej (advisor) ; Zouhar, Jan (referee)
In this thesis the heterogeneity of regional real estate prices in United States is investigated. A high dimensional VAR model with additional exogenous predictors, originally introduced by \cite{fan11}, is adopted. In this framework, the common factor in regional house prices dynamics is explained by exogenous predictors and the spatial dependencies are captured by lagged house prices in other regions. For the purpose of estimation and variable selection under high-dimensional setting the concept of Penalized Least Squares (PLS) with different penalty functions (e.g. LASSO penalty) is studied in detail and implemented. Moreover, clustering methods are employed to identify subsets of statistical regions with similar house prices dynamics. It is demonstrated that these clusters are well geographically defined and contribute to a better interpretation of the VAR model. Next, we make use of the LASSO variable selection property in order to construct the impulse response functions and to simulate the prices behavior when a shock occurs. And last but not least, one-period-ahead forecasts from VAR model are compared to those from the Diffusion Index Factor Model by \cite{stock02}, a commonly used model for forecasts.
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