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Multidimensional Data Analysis and Analytic View Processing
Foltýnová, Veronika ; Burget, Radim (referee) ; Škorpil, Vladislav (advisor)
This thesis deals with the analysis and display of multidimensional data. In the theoretical part, the issue of data mining, its tasks and techniques, and a brief explanation of the terms Business Intelligence and data warehouse are presented. The issue of databases is also described in this thesis. Subsequently, the options for displaying multidimensional data are described. At the end of the theoretical part is briefly explained the problems of optical networks and especially the terms Gigabit passive optical network and its frame, because the data from the frames of this network will be displayed by an application. In the practical part, you can find creating a source database and an application to create a OLAP cube and display multidimensional data. This application is based on the theoretical knowledge of multidimensional databases and OLAP technology.
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Analysis and Structure Recommendations for E-commerce Site Optimization
Bureš, Petr ; Hudák, Michal (referee) ; Dydowicz, Petr (advisor)
This bachelor‘s thesis focuses on analysis of optimization of e-commerce website. The aim of this work is to outline the optimization solutions from different point of views including online marketing, information architecture, web analytics and other weak points identified by the analysis. Several approaches of methodology will be used to support the analysis, such as data mining analysis of a shopping basket integrated within e-commerce website. The economic benefits of the final optimisation solutions will be assessed and the recommendations will be set out.
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Competitive Intelligence
Mikuš, Ondřej ; Karásek, Jan (referee) ; Bartes, František (advisor)
The diploma thesis focuses on the practical use of competitive intelligence, a method increasingly used to support decision making nowadays. The essential key principle is based on collection of precise data and their thourough analysis. The obtained information gives a comprehensive overview of the competitor analysis. The acquired knowledge of the competition is important for company's strategic decision-making processes. At the beginning the thesis gives a theoretical background on different means of data collecting including the ethic kodex. Folowing is the identification of the modern sources of information. Further, various methods and approaches of competitive intelligence are described. And the theoretical background on competitive strategies and their impact on the market wraps up the theoretical part of the thesis. The method is applied to create departmet of competitive inteligence for the company. Then the various options for further go to market activities are described and the scope of cooperation with selected customers is evaluted. The thesis describes the competitive intelligence advantages and the possible ways of aplications for the competitive strategy. At the end, the recommendations for the management of the company are summed up.
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Mining Multiple Level Association Rules
Nguyenová, Thanh Lam ; Burget, Radek (referee) ; Bartík, Vladimír (advisor)
This bachelor thesis deals with multiple level association rules mining. The aim of this work is to focus on available algorithms for mining multiple level association rules and to implement an application with a graphical user interface that will demonstrate the functionality of these algorithms. Five algorithms based on the Apriori algorithm were chosen. Experiments with each algorithm were performed using the application and the results were compared and evaluated at the end of the thesis.
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Knowledge Discovery from Web Logs
Valaštín, Samuel ; Rychlý, Marek (referee) ; Bartík, Vladimír (advisor)
This bachelor thesis deals with the problem of knowledge discovery from web logs. The data source in the form of web access logs allows, after appropriate preprocessing, the use of a number of techniques that are designed to deal with knowledge discovery. By applying these techniques to preprocessed data, it is possible to classify user behavior into groups, to discover interesting associations in user behavior, or to discover previously unknown sequences in common user behavior.
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Classification on unbalanced data
Hlosta, Martin ; Popelínský, Lubomír (referee) ; Štěpánková,, Olga (referee) ; Zendulka, Jaroslav (advisor)
Tématem této disertační práce je klasifikace daty s nevyváženými daty. Jedná se o oblast strojového, jejímž cílem je řešit problémy, které plynou z toho, že jedna ze tříd je v datech zastoupena výrazně méně než třída druhá. Minoritní třída má často větší význam a tradiční metody upřednostňující majoritní třídu nedosahují dobrých výsledků na třídě minoritní. Dvě aplikační domény motivovaly výzkum a vedly na identifikaci dvou specifických, dosud neřešených problémů. V první z nich vedlo omezení kladené na minimální požadovanou přesnost na minoritní třídě v počítačové bezpečnosti na formulaci úlohy klasifikace s omezením. Navrhl jsem metodu, která kombinuje upravenou verzi logistické regrese a stochastické algoritmy, které vždy vylepšily výsledky logistické regrese.Druhou je doména analýzy učení (Learning Analytics), která motivovala definici problému predikce splnění cíle, jenž má specifikovaný termín splnění. Byl představen koncept sebe-učení (Self-Learning), kdy trénování modelu probíhá díky jedincům, kteří tento cíl splní předčasně. Díky malému počtu jedinců splňujících úlohu na začátku je problém silně nevyvážený, ale nevyváženost klesá směrem k termínu splnění. Na problému identifikace rizikových studentů distanční univerzity bylo ukázáno, že (1) takový koncept dává lepší výsledky než specifikovaná základna (baseline), (2) a že metody pro vypořádání se s nevyvážeností, které neberou v potaz informaci o doméně, nevedly k velkým zlepšením. Evaluace ukázala, že metody založené na znalosti domény v rozšířené verzi pro Self-Learning vylepšily klasifikaci více než běžné metody pro vypořádání se s nevyvážeností a že znalost příčiny nevyváženosti může vést k lepším výsledkům.
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Methods of Social Network Analysis for Data Mining
Machulka, Tomáš ; Rozman, Jaroslav (referee) ; Samek, Jan (advisor)
This bachelor thesis describes some of many methods for social network analysis. There is also a description of the data visualization. The thesis contains description of implementation of aplication for social network analysis using several methods. Output of analysis is confronted with output of other software for social network analysis.
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Data Mining with Python
Krestianková, Tamara ; Burgetová, Ivana (referee) ; Zendulka, Jaroslav (advisor)
This thesis deals with principles of data mining process, available Python packages for data mining and a demonstration of Python script capable of data analyisis focused on classification techniques. Created classifiers are able to classify subjects into two groups - healthy people and people suffering from Parkinson's disease - based on their biomedical vocal analysis data.
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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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Web Application of Recommender System
Koníček, Igor ; Bartík, Vladimír (referee) ; Zendulka, Jaroslav (advisor)
This master's thesis describes creation of recommender system that is used in real server cbdb.cz. A~fully operational recommender system was developed using collaborative and content-based filtering techniques. Thanks to many user feedback, we were able to evaluate their opinion. Many recommended books were tagged as desirable. This thesis is extending current functionality of cbdb.cz with recommender system. This system uses its extensive database of ratings, users and books.
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