Knowledge discovery on consumers' behaviour

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62156489%3A43110%2F13%3A00213762" target="_blank" >RIV/62156489:43110/13:00213762 - isvavai.cz</a>

Výsledek na webu

—

DOI - Digital Object Identifier

—

Jazyk výsledku

angličtina

Název v původním jazyce

Knowledge discovery on consumers' behaviour

Popis výsledku v původním jazyce

This paper summarizes results of the research project "Application of modern methods to data processing in the field of marketing research" which was solved at the Department of Informatics, Faculty of Business and Economics of Mendel University in Brno.The most of these results were presented at international conferences. It describes the use of knowledge discovery techniques on data from marketing research of consumers' behaviour. The paper deals with issues of classification, cluster analysis, correlation and association rules. For classification there were used various algorithms: multi-layer perceptron neural network, self-organizing (Kohonen's) maps, bayesian networks and generation of a decision tree. Beside Kohonen's maps, which were tested inMATLAB software, all classification methods were tested in Weka software. In order to find clusters of the methods K-means, Expectation-Maximization, DBSCAN Weka was also used as software for clustering. Correlation analysis was done bas

Název v anglickém jazyce

Knowledge discovery on consumers' behaviour

Popis výsledku anglicky

This paper summarizes results of the research project "Application of modern methods to data processing in the field of marketing research" which was solved at the Department of Informatics, Faculty of Business and Economics of Mendel University in Brno.The most of these results were presented at international conferences. It describes the use of knowledge discovery techniques on data from marketing research of consumers' behaviour. The paper deals with issues of classification, cluster analysis, correlation and association rules. For classification there were used various algorithms: multi-layer perceptron neural network, self-organizing (Kohonen's) maps, bayesian networks and generation of a decision tree. Beside Kohonen's maps, which were tested inMATLAB software, all classification methods were tested in Weka software. In order to find clusters of the methods K-means, Expectation-Maximization, DBSCAN Weka was also used as software for clustering. Correlation analysis was done bas

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

AE - Řízení, správa a administrativa

OECD FORD obor

—

Projekt

—

Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2013

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis

ISSN

1211-8516

e-ISSN

—

Svazek periodika

61

Číslo periodika v rámci svazku

7

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

9

Strana od-do

2893-2901

Kód UT WoS článku

—

EID výsledku v databázi Scopus

—