Exploring How Customers Shop for Meat Products

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62156489%3A43110%2F14%3A00213758" target="_blank" >RIV/62156489:43110/14:00213758 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Exploring How Customers Shop for Meat Products

Popis výsledku v původním jazyce

This contribution contains problems of marketing research data classification by means of data mining algorithms. Three basic methods are described, classification with the aid of Multi-layer Perceptron neural network with Back-propagation algorithm, classification with the aid of Bayesian Networks and classification with the aid of Decision Tree. Finally, applicability of these algorithms is compared. These algorithms are applied over the data from a survey about consumer behavior in the food market inthe Czech Republic (n = 1127, data collection in 2011). The data were further analyzed with statistical tools, such as cluster analysis and analysis of contingency. The best achieved result was 42.65% by method LMT. Although this level may seem to be relatively low, due to the fact that also the dependencies between individual 20 factors and the 5 possible store loyalty options revealed by analysis of contingency were not strong, this result shows that the tools can reach relatively hig

Název v anglickém jazyce

Exploring How Customers Shop for Meat Products

Popis výsledku anglicky

This contribution contains problems of marketing research data classification by means of data mining algorithms. Three basic methods are described, classification with the aid of Multi-layer Perceptron neural network with Back-propagation algorithm, classification with the aid of Bayesian Networks and classification with the aid of Decision Tree. Finally, applicability of these algorithms is compared. These algorithms are applied over the data from a survey about consumer behavior in the food market inthe Czech Republic (n = 1127, data collection in 2011). The data were further analyzed with statistical tools, such as cluster analysis and analysis of contingency. The best achieved result was 42.65% by method LMT. Although this level may seem to be relatively low, due to the fact that also the dependencies between individual 20 factors and the 5 possible store loyalty options revealed by analysis of contingency were not strong, this result shows that the tools can reach relatively hig

Druh

D - Stať ve sborníku

CEP obor

AE - Řízení, správa a administrativa

OECD FORD obor

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Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2014

Kód důvěrnosti údajů

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

Název statě ve sborníku

Recent Advances in Economics, Management and Marketing

ISBN

978-960-474-364-3

ISSN

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e-ISSN

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Počet stran výsledku

5

Strana od-do

50-54

Název nakladatele

WSEAS Press

Místo vydání

Cambridge, MA, USA

Místo konání akce

Cambridge, MA, USA

Datum konání akce

1. 1. 2014

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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