Academic literature on the topic 'Shluková analýza dat'

Cílem empirické studie je zjistit, jaké učební styly studenti preferují v souvislosti s tendencí vyhledávat mimořádné prožitky a jaké tvoří přirozené skupiny (shluky) z hlediska těchto dvou proměnných, pohlaví a příslušnosti ke všeobecně či tělovýchovně zaměřené gymnaziální třídě. V první části jsou představena teoretická východiska, kterými jsou teorie stylu učení autorky Dunnové a koncept tendence vyhledávat mimořádné prožitky autora Zuckermana. Druhá část příspěvku popisuje použité výzkumné metody včetně ověření reliability a validity použitého nástroje pro sběr dat, kterým je dotazník. Metodou zpracování dat je shluková analýza, která zprostředkovává vhled do velkého počtu proměnných. Třetí část studie seznamuje s hlavními výsledky, které diskutuje a zpracovává do doporučení do praxe. Nejvýraznější rozdíly mezi pohlavími a příslušností k typu třídy byly zjištěny v tendenci vyhledávat mimořádné prožitky a částečně ve stylech učení. Dívky sportovních tříd nejméně vyhledávají napětí a dobrodružství, nevyhledávají nové zkušenosti, jsou nejvíce rezistentní vůči nudě a nejvíce respektují normy a pravidla. Chlapci jsou nejčastěji členy shluku, který je charakteristický nevyhledáváním napětí a dobrodružství, vyhledáváním nových zkušeností, prožitků prostřednictvím nerespektování norema nízkou rezistencí vůči nudě. Chlapci ze sportovně zaměřených tříd mají nízkou odpovědnost při učení, úkoly potřebují mít strukturované a preferují zážitkové učení. Z uvedeného vyplývá, že preference učebního stylu a tendence vyhledávat mimořádné prožitky může u gymnaziálních studentů souviset, stejně tak může souviset s pohlavím a příslušností k typu třídy.

The main purpose of panel studies is to analyze changes in values of studied variables over time. In micro panel research, a large number of elements are periodically observed within the relatively short time period of just a few years. Moreover, the number of repeated measurements is small. This dissertation deals with contemporary approaches to the regression and the clustering analysis of micro panel data. One of the approaches to the micro panel analysis is to use multivariate statistical models originally designed for crosssectional data and modify them in order to take into account the within-subject correlation. The thesis summarizes available tools for the regression analysis of micro panel data. The known and currently used linear mixed effects models for a normally distributed dependent variable are recapitulated. Besides that, new approaches for analysis of a response variable with other than normal distribution are presented. These approaches include the generalized marginal linear model, the generalized linear mixed effects model and the Bayesian modelling approach. In addition to describing the aforementioned models, the paper also includes a brief overview of their implementation in the R software. The difficulty with the regression models adjusted for micro panel data is the ambiguity of their parameters estimation. This thesis proposes a way to improve the estimations through the cluster analysis. For this reason, the thesis also contains a description of methods of the cluster analysis of micro panel data. Because supply of the methods is limited, the main goal of this paper is to devise its own two-step approach for clustering micro panel data. In the first step, the panel data are transformed into a static form using a set of proposed characteristics of dynamics. These characteristics represent different features of time course of the observed variables. In the second step, the elements are clustered by conventional spatial clustering techniques (agglomerative clustering and the C-means partitioning). The clustering is based on a dissimilarity matrix of the values of clustering variables calculated in the first step. Another goal of this paper is to find out whether the suggested procedure leads to an improvement in quality of the regression models for this type of data. By means of a simulation study, the procedure drafted herein is compared to the procedure applied in the kml package of the R software, as well as to the clustering characteristics proposed by Urso (2004). The simulation study demonstrated better results of the proposed combination of clustering variables as compared to the other combinations currently used. A corresponding script written in the R-language represents another benefit of this paper. It is available on the attached CD and it can be used for analyses of readers own micro panel data.

The topic of the present thesis is an analysis of a sample data archive containing measured values of real and reactive power. The measurement in question took place in late 2006 and early 2007 using MEg40 recording measurement devices disposed in a station for transforming high voltage to low voltage in the Pražská energetika distribution network. The procedure of processing measured values, the preparation thereof for a subsequent processing by a neural network, and a final statistical evaluation of determined individual clusters -- typical daily take-off diagrams -- will be described. The results of the present thesis may be applied in the making of predictions of electrical energy consumption at a particular transformer station.

Abstract Cluster analysis has become one of the main tools used in extracting knowledge from data, which is known as data mining. In this area of data analysis, data of large dimensions are often processed, both in the number of objects and in the number of variables, which characterize the objects. Many methods for data clustering have been developed. One of the most widely used is a k-means method, which is suitable for clustering data sets containing large number of objects. It is based on finding the best clustering in relation to the initial distribution of objects into clusters and subsequent step-by-step redistribution of objects belonging to the clusters by the optimization function. The aim of this Ph.D. thesis was a comparison of selected variants of existing k-means methods, detailed characterization of their positive and negative characte- ristics, new alternatives of this method and experimental comparisons with existing approaches. These objectives were met. I focused on modifications of the k-means method for clustering of large number of objects in my work, specifically on the algorithms BIRCH k-means, filtering, k-means++ and two-phases. I watched the time complexity of algorithms, the effect of initialization distribution and outliers, the validity of the resulting clusters. Two real data files and some generated data sets were used. The common and different features of method, which are under investigation, are summarized at the end of the work. The main aim and benefit of the work is to devise my modifications, solving the bottlenecks of the basic procedure and of the existing variants, their programming and verification. Some modifications brought accelerate the processing. The application of the main ideas of algorithm k-means++ brought to other variants of k-means method better results of clustering. The most significant of the proposed changes is a modification of the filtering algorithm, which brings an entirely new feature of the algorithm, which is the detection of outliers. The accompanying CD is enclosed. It includes the source code of programs written in MATLAB development environment. Programs were created specifically for the purpose of this work and are intended for experimental use. The CD also contains the data files used for various experiments.

The diploma thesis called Cluster Analysis Czech 100 Companies on the Basis of Financial Statements deals with the testing of two hypotheses using a multidimensional statistical method - cluster analysis. The input data for the application of statistical methods are financial statements of selected companies for the years 2014 and 2015 which are publicly available. These data are digitized and subjected to methods of financial analysis.

Tato práce se zabývá technologiemi velkých dat v kontextu měření parametrů sítě. Popisuje téma velkých dat a jejich využití, představuje základní parametry sítě, jejich měření a metody zhodnocení. Vyhodnocuje RTR NetTest aplikaci, testovací proceduru a měřené parametry. Byla vytvořena skupina nástrojů pro posouzení základních kvantitativních parametrů mobilní sítě na základě dat z databáze RTR. Rozbor denního efektu shrnuje časovou proměnlivost sítě. Chování v prostoru je posouzeno binováním a shlukovou analýzou, současně se srovnáním řízeného testování a crowdsourcingu.

S rostoucím množstvím shromažďovaných a ukládaných dat vzniká potřeba shlukovacích metod, které by se dokázaly vypořádat i s rozsáhlými datovými soubory. Proto se objevuje množství nových algoritmů, vycházejících jak ze statistických přístupů, tak i z oblasti strojového učení. Cílem této diplomové práce je stručně představit dostupné metody shlukové analýzy a zhodnotit jejich silné a slabé stránky při analýze velkých souborů. Obsahem teoretické části je shrnutí základních pojmů a principů, které jsou všem metodám společné, a popisu nejznámějších metod shlukové analýzy. Ten obsahuje stručné vysvětlení, na jakém principu fungují a jaké výhody nebo případné nedostatky můžeme při jejich použití očekávat. Praktická část práce je věnována vlastnímu testování osmi metod dostupných v komerčním (SPSS, S-PLUS, STATISTICA) nebo akademickém (Weka) softwaru. Pro testování jsou použity umělé soubory se specifickými charakteristikami, které jsem vygeneroval pomocí vlastního algoritmu. Ten je rozšířením Neyman-Scottova procesu a kromě sférických shluků generuje i shluky nepravidelných tvarů. Výsledky potvrzují očekávání vycházející z teoretických předpokladů. Přinášejí však možnost kvantifikace vlivu charakteru dat na vhodnost jednotlivých metod.

This master's thesis deals with development of a module for a data mining system, which is being developed on FIT. The first part describes the general knowledge discovery process and cluster analysis including cluster validation; it also describes Oracle Data Mining including algorithms, which it uses for clustering. At the end it deals with the system and the technologies it uses, such as NetBeans Platform and DMSL. The second part describes design of a clustering module and a module used to compare its results. It also deals with visualization of cluster analysis results and shows the achievements.

Nowadays quality of life is often discussed topic. In defining this term, there is considerable ambiguity and disunity, since there is no universally accepted definition, nor theoretically sophisticated model. However, despite this fact, the level of quality of life is currently one of the most discussed topic. Monitoring the quality of life by using a variety of indicators are engaged in several international organizations, one of them is the Development Programme of the United Nations. This organization annually publishes the Human Development Index, which divides the world´s countries into four groups according to their level of development: low, medium, high and very high development. The aim of this thesis is to analyze the quality of life in 125 countries by using cluster analysis, accurately the Ward's method. Quality of life in this thesis is evaluated based on 19 demographic and economic indicators, which include life expectancy, literacy rate, access to drinking water and infant mortality rate. The cluster analysis divided the country into individual clusters by their similarities. Six clusters were created by this analysis, which had been compared with the results of Human Development Index. The clusters very well reflect the division, which is commonly used in the characterization of developing and developed countries. Each of the six clusters can be very well described and characterized in terms of quality of life. It is also possible qualify those clusters as poorest developing, low developed, moderately developed, medium development, high and very high development countries. Based on the results it can be stated that this analysis is consistent with other indicators of quality of life and the resulting clusters are identical with the division of countries which is commonly used.

At the beginning, the work describes DNS and SSL/TLS protocols, it mainly deals with communication between devices using these protocols. Then we'll talk about data preprocessing and data cleaning. Furthermore, the thesis deals with basic data mining techniques such as data classification, association rules, information retrieval, regression analysis and cluster analysis. The next chapter we can read something about how to identify mobile devices on the network. We will evaluate data sets that contain collected data from communication between the above mentioned protocols, which will be used in the practical part. After that, we finally get to the design of a system for analyzing network communication data. We will describe the libraries, which we used and the entire system implementation. We will perform a large number of experiments, which we will finally evaluate.

This thesis deals with the design and implementation of a cluster analysis module for currently developing datamining system DataMiner on FIT BUT. So far, the system lacked cluster analysis module. The main objective of the thesis was therefore to extend the system of such a module. Together with me, Pavel Riedl worked on the module. We have created a common part for all the algorithms so that the system can be easily extended to other clustering algorithms. In the second part, I extended the clustering module by adding three density based clustering aglorithms - DBSCAN, OPTICS and DENCLUE. Algorithms have been implemented and appropriate sample data was chosen to verify theirs functionality.