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Classification and Regression Trees in R
Nemčíková, Lucia ; Bašta, Milan (advisor) ; Vilikus, Ondřej (referee)
Tree-based methods are a nice add-on to traditional statistical methods when solving classification and regression problems. The aim of this master thesis is not to judge which approach is better but rather bring the overview of these methods and apply them on the real data using R. Focus is made especially on the basic methodology of tree-based models and the application in specific software in order to provide wide range of tool for reader to be able to use these methods. One part of the thesis touches the advanced tree-based methods to provide full picture of possibilities.
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Use of Methods of Managerial Decision-Making in foundation of the new enterprise on the market
Oberhel, Martin ; Pudil, Pavel (advisor) ; Bína, Vladislav (referee)
This thesis is focused on the methods and tools which are helpful in decision-making under uncertainty and risk. The methods of decision-making for discrete and continuous values of risk factors are used in the thesis. In case of discrete values of risk factors and decision-making under risk, the thesis uses the rule of expected values, the rule of expected value and variance and also calculates the value of perfect information. In case of decision-making under uncertainty, the thesis is focused on the rule of maximin and maximax, Laplace's rule, Hurwitz's rule and Savage's rule. The following part of the thesis is devoted to decision-making with continuous values of risk factors. It utilizes the Monte Carlo simulation method and the sensitivity analysis with the help of Lumina Analytica software. The last part of the thesis is aimed at utilization of decision trees in case of multistage decision-making. It uses the Treeplan software which works as a plugin in MS office Excel. All the mentioned methods are practically applied to a concrete case of analysing and ex post evaluating the business plans of a company, which is based at Jindřichův Hradec market.
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The use of statistical methods in data mining in predicting consumer behaviour for Internet purchases
Podzimková, Michaela ; Vilikus, Ondřej (advisor) ; Berka, Petr (referee)
Data mining is a new discipline that occurs with increasing amount of stored data and the increasing need to obtain the information hidden in them. It is focused on the mining of potentially useful information from large data sets and it lies at the intersection of statistics, machine learning, artificial intelligence, databases and other areas. The aim of this thesis is to present the process of data mining with an emphasis on its connection with statistics and to describe a selection of statistical methods widely used in this field and which were also used in the applied data mining problem in this thesis. Real data from purchases in the online store show that using different methods gives different results and interesting information about purchasing behavior, and also proves that not all methods are always applicable to all types of tasks.
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Převod vybraných algoritmů data-mining z jazyka Java do binární (.exe) formy
Šrom, Jakub
There are many successful systems for data-mining (eg. WEKA, RapidMiner, etc.), which currently hold many algorithms implemented in Java, which allows their use under different operating systems. The disadvantage of the interpreted source code is a slowdown in the calculation and limited memory usage. The thesis is focused on the transfer of several selected implementations of algorithms in Java binaries (.exe) through the conversion of source code in C ++ under MS Windows 7 64-bit. The aim is to speed up calculations and improve management of memory usage. Binary form must give identical results as the original form. In addition to the actual transfer, the thesis also includes comparing time and memory requirements of the original (using the Java Runtime Environment, JRE) interpreted implementation in Java (JRE 64-bit) and x64 resulting binary forms, for selected test data.
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Aplikace metod strojového učení na dolování znalosti z dat
Kraus, Jan
The diploma thesis deals with the area of data mining applied to large collections of textual data. Specifically the thesis is focused on sentiment analysis based on the user's subjective verbal assessment in natural language. The first part of the diploma thesis introduces the reader to basic terms of machine learning and data mining applied particularly to large textual data collections. Following is the description of textual data preprocessing methods and principles of machine learning algorithms. In the practical part of this thesis there are experiments designed and subsequently executed using the SPSS Modeler tool. The experimental part is focused especially on identification of significant attributes and recongnition of relationships between them. The emphasis is put especially on thorough interpretation of the results obtained.
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Datamining - teorie a praxe
Popelka, Aleš ; Maryška, Miloš (advisor) ; Machač, Ivo (referee)
This thesis deals with the topic of the technology called data mining. First, the thesis describes the term data mining as an independent discipline and then its processing methods and the most common use. The term data mining is thereafter explained with the help of methodologies describing all parts of the process of knowledge discovery in databases -- CRISP-DM, SEMMA. The study's purpose is presenting new data mining methods and particular algorithms -- decision trees, neural networks and genetic algorithms. These facts are used as theoretical introduction, which is followed by practical application searching for causes of meningoencephalitis development of certain sample of patients. Decision trees in system Clementine, which is one of the top datamining tools, were used for the analysys.
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Using data mining to manage an enterprise.
Prášil, Zdeněk ; Pour, Jan (advisor) ; Novotný, Ota (referee)
The thesis is focused on data mining and its use in management of an enterprise. The thesis is structured into theoretical and practical part. Aim of the theoretical part was to find out: 1/ the most used methods of the data mining, 2/ typical application areas, 3/ typical problems solved in the application areas. Aim of the practical part was: 1/ to demonstrate use of the data mining in small Czech e-shop for understanding of the structure of the sale data, 2/ to demonstrate, how the data mining analysis can help to increase marketing results. In my analyses of the literature data I found decision trees, linear and logistic regression, neural network, segmentation methods and association rules are the most used methods of the data mining analysis. CRM and marketing, financial institutions, insurance and telecommunication companies, retail trade and production are the application areas using the data mining the most. The specific tasks of the data mining focus on relationships between marketing sales and customers to make better business. In the analysis of the e-shop data I revealed the types of goods which are buying together. Based on this fact I proposed that the strategy supporting this type of shopping is crucial for the business success. As a conclusion I proved the data mining is methods appropriate also for the small e-shop and have capacity to improve its marketing strategy.
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Classification of strategical plans under conditions of the risk {--} decision making of investment by the apparatus of decision trees
JÍCHOVÁ, Romana
In my thesis I dealt with the capital decision making, with the methods to classification of the investments and with decision making under risk and uncertainty. The aim of the thesis was the application of mathematical methods by selection the options of the investments. The main task was to show the possibility of using decision trees, which are the graphical instruments for describing actions available to the decision maker. In the practical part there is described the process of making a decision tree on the example of the sale of real properties and on the example of the extraction of coal oil.
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Split Softening as a Problem of Machine Learning
Dvořák, Jakub
Softening splits in decision trees has ability to grow up quality of a classifier. This article concerns with some aspects of an optimization task to find such softening that reaches the best classification on training data. Experiments show, that improvement occurs on test data as well.
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Finding Optimal Decision Trees
Máša, Petr ; Ivánek, Jiří (advisor) ; Berka, Petr (referee) ; Jiroušek, Radim (referee)
Rozhodovácí stromy jsou rozšířenou technikou pro popis dat. Používají se často teké pro predikace. Zajímavým problémemje, že konkrétní distribuce může být popsána jedním či více rozhodovacími stromy.Obvykle nás zajímá co nejjednodušší rozhodovací strom(který budeme nazývat též optimální rozhodovací strom).Tato práce navrhuje rozšíření prořezávácí fáze algoritmů pro rozhodovací stromytak, aby umožňovala více prořezávání. V práci byly zkoumány teoretické i praktické vlastnosti tohoto rozšířeného algoritmu. Jako hlavní teoretický výsledek bylo dokázano, že pro jistou třídu distribucí nalezne algoritmus optimální rozhodovací strom(tj.nejmenší rozhodovací strom, který reprezentuje danou distribuci). V praktických testech bylo zkoumáno, jak je schopen algoritmus rekonstruovat známý strom z dat. Zajímalo nás, zdali dosáhne naše rozšíření zlepšení v počtu správně rekonstruovaných stromů zejména v případě, že data jsou dodatečně velká ( z hlediska počtu záznamů). Tato doměnka byla potvrzena praktickými testy. Obdobný výsledek byl před několika lety prokázán pro Bayesovské sítě. Algoritmus navržený v této disertační práci je polynomiální v počtu listů stromu, který je výstupem hladového algoritmu pro růst stromů, což je vylepšení oproti jednoduchému algoritmu prohledávání všech možných stromů, který je exponenciální.
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