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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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Association Rules Mining
Dvořák, Michal ; Chmelař, Petr (referee) ; Stryka, Lukáš (advisor)
The main goal of this bachelor's thesis is design and implementation of the application that provides a comparison of the performance and time consumption of given algorithms for mining of the frequent itemsets and the association rules. For demonstration, the mining algorithms Apriori, AprioriTIDList, AprioriItemSet and the method using FP-tree were chosen. The tests were executed over various amounts of data and with different minimum support and confidence values as well. The application was implemented in the object oriented language C# and the relational database provided by MS SQL Server 2008 is used as the data source.
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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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Association Rules Mining
Dvořák, Michal ; Chmelař, Petr (referee) ; Stryka, Lukáš (advisor)
The main goal of this bachelor's thesis is design and implementation of the application that provides a comparison of the performance and time consumption of given algorithms for mining of the frequent itemsets and the association rules. For demonstration, the mining algorithms Apriori, AprioriTIDList, AprioriItemSet and the method using FP-tree were chosen. The tests were executed over various amounts of data and with different minimum support and confidence values as well. The application was implemented in the object oriented language C# and the relational database provided by MS SQL Server 2008 is used as the data source.
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Frequent Pattern Discovery in a Data Stream
Dvořák, Michal ; Hlosta, Martin (referee) ; Zendulka, Jaroslav (advisor)
Frequent-pattern mining from databases has been widely studied and frequently observed. Unfortunately, these algorithms are not suitable for data stream processing. In frequent-pattern mining from data streams, it is important to manage sets of items and also their history. There are several reasons for this; it is not just the history of frequent items, but also the history of potentially frequent sets that can become frequent later. This requires more memory and computational power. This thesis describes two algorithms: Lossy Counting and FP-stream. An effective implementation of these algorithms in C# is an integral part of this thesis. In addition, the two algorithms have been compared.
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