Academic literature on the topic 'Zhlukovanie'

The aim of this master's thesis was to get acquainted with cluster analysis, clustering methods and their theoretical properties. It was necessary select clustering algorithms whose properties will be analyzed, find and select data sets on which these algorithms will be triggered. Also, the goal was to design and implement an application that will evaluate and display clustering results in an appropriate manner. The last step was to analyze the results and compare them with theoretical assumptions.

This master thesis deals with cluster analysis, more specifically with clustering methods that use fuzzy sets. Basic clustering algorithms and necessary multivariate transformations are described in the first chapter. In the practical part, which is in the third chapter we apply fuzzy c-means clustering and k-means clustering on real data. Data used for clustering are the inputs of chemical transport model CMAQ. Model CMAQ is used to approximate concentration of air pollutants in the atmosphere. To the data we will apply two different clustering methods. We have used two different methods to select optimal weighting exponent to find data structure in our data. We have compared all 3 created data structures. The structures resembled each other but with fuzzy c-means clustering, one of the clusters did not resemble any of the clustering inputs. The end of the third chapter is dedicated to an attempt to find a regression model that finds the relationship between inputs and outputs of model CMAQ.

Táto diplomová práca sa zaoberá novým prístupom k zhlukovaniu časových rád na základe dynamického faktorového modelu. Dynamický faktorový model je technika redukujúca dimenziu a rozširuje klasickú faktorovú analýzu o požiadavku autokorelačnej štruktúry latentných faktorov. Parametre modelu sa odhadujú pomocou EM algoritmu za použitia Kalmanovho filtra a vyhladzovača a taktiež sú aplikované nevyhnutné podmienky na model, aby sa stal identifikovateľným. Po tom, ako je v práci predstavený teoretický koncept prístupu, dynamický faktorový model je aplikovaný na skutočné pozorované časové rady a práca skúma jeho správanie a vlastnosti na jednomesačných meteorologických dátach požiarneho indexu (Fire Weather Index) na 108 požiarnych staniciach umiestnených v Britskej Kolumbii. Postup výpočtu modelu odhadne záťažovú maticu (loadings matrix) spolu so zodpovedajúcim malým počtom latentných faktorov a kovariačnou maticou modelovaných časových rád. Diplomová práca aplikuje k-means zhlukovanie na výslednú záťažovú maticu a ponúka rozdelenie meteorologických staníc do zhlukov založené na redukovanej dimenzionalite pôvodných dát. Vďaka odhadnutým priemerom zhlukov a odhadnutým latentným faktorom je možné získať aj priemerné trendy každého zhluku. Následne sú dosiahnuté výsledky porovnané s výsledkami získanými na dátach z rovnakých staníc avšak iného mesiaca, aby sa stanovila stabilita zhlukovania. Práca sa taktiež zaoberá efektom varimax rotácie záťažovej matice. Diplomová práca naviac navrhuje metódu detekovania odľahlých časových rád založenú na odhadnutej kovariačnej matici modelu a rozoberá dôsledky odľahlých hodnôt na odhanutý model.

Goal of this master's thesis was in cooperation with the company Avast to design a system, which can extract knowledge from a database of graphs. Graphs, used for data mining, describe behaviour of computer systems and they are anonymously inserted into the company's database from systems of the company's products users. Each graph in the database can be assigned with one of two labels: clean or malware (malicious) graph. The task of the proposed self-learning system is to find clusters of graphs in the graph database, in which the classes of graphs do not mix. Graph clusters with only one class of graphs can be interpreted as different types of clean or malware graphs and they are a useful source of further analysis on the graphs. To evaluate the quality of the clusters, a custom metric, named as monochromaticity, was designed. The metric evaluates the quality of the clusters based on how much clean and malware graphs are mixed in the clusters. The best results of the metric were obtained when vector representations of graphs were created by a deep learning model (variational  graph autoencoder with two relation graph convolution operators) and the parameterless method MeanShift was used for clustering over vectors.

This thesis proposes and implements a system for movement analysis of vehicles on crossroads. It detects and tracks the movement of vehicles in the video, gained from the stationary video camera, which has the view of some crossroad. The trajectories are stored and their number and directions are analysed. The detection was made using cascade classifier. A dataset of 10500 positive and 10500 negative samples has been created to train the classifier. Vehicles are tracked using KCF method. For trajectory clustering, needed by analysis, the Mean Shift method is used. Testing showed, that the overall success of vehicle movement analysis is 92.77%.

The master's thesis deals with biometric systems, especially these based on human recognition by finger veins. It describes some development principles of the new biometric system. It proposes some new approaches to the comparison of finger vein patterns and their fast identification in sizable databases. Verification is based on templates comparison by similarity and distance measures with proposed alignment approaches. The proposed method of identification is based on the combination of clustering and genetic algorithm. The second option is using the indexing tree structure and searching by range query.