Thesis Details
Metody pro získávání asociačních pravidel z dat
The aim of this thesis is to implement Multipass-Apriori method for mining association rules from text data. After the introduction to the field of knowledge discovery, the specific aspects of text mining are mentioned. In the mining process, preprocessing is a very important problem, use of stemming and stop words dictionary is necessary in this case. Next part of thesis deals with meaning, usage and generating of association rules. The main part is focused on the description of Multipass-Apriori method, which was implemented. On the ground of executed tests the most optimal way of dividing partitions was set and also the best way of sorting the itemsets. As a part of testing, Multipass-Apriori method was compared with Apriori method.
frequent itemset, association rules, Apriori, Multipass-Apriori, stemming, stop words, text data preprocessing,
Hanáček Petr, doc. Dr. Ing. (DITS FIT BUT), člen
Krejčíček Jaromír, prof. Ing., CSc. (UNOB), člen
Křena Bohuslav, Ing., Ph.D. (DITS FIT BUT), člen
Sumec Stanislav, Ing., Ph.D. (DCGM FIT BUT), člen
Zbořil František, doc. Ing., Ph.D. (DITS FIT BUT), člen
@mastersthesis{FITMT4771,
author = "Martin Uhl\'{i}\v{r}",
type = "Master's thesis",
title = "Metody pro z\'{i}sk\'{a}v\'{a}n\'{i} asocia\v{c}n\'{i}ch pravidel z dat",
school = "Brno University of Technology, Faculty of Information Technology",
year = 2007,
location = "Brno, CZ",
language = "czech",
url = "https://www.fit.vut.cz/study/thesis/4771/"
}