Application of data-mining techniques in customer segmentation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41110%2F11%3A51189" target="_blank" >RIV/60460709:41110/11:51189 - 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

Application of data-mining techniques in customer segmentation

Popis výsledku v původním jazyce

The paper discusses the possibilities of using the various methods of customer segmentation by mining the information in the databases. The data-mining model was constructed from approximately 60 thousand transaction records. Only food records were selected for the analysis. The partial objective of the paper was to examine the possibilities of data preparation such as restructuralization, logarithmic transformation, and dimensionality reduction. The main goal of the paper was to find meaningful patterns in the analyzed data and identify clusters of customers with similar behavior and needs. The segmentation was realized by various data mining techniques as follows: K-means clustering, Two Step clustering, and unsupervized algorithm based on neural networks called Self-Organizing Maps. The quality of results was evaluated by the Silhouette measure, which combines the principles of clusters separation and cohesion. After that the detailed analysis of the final segments was done.

Název v anglickém jazyce

Application of data-mining techniques in customer segmentation

Popis výsledku anglicky

The paper discusses the possibilities of using the various methods of customer segmentation by mining the information in the databases. The data-mining model was constructed from approximately 60 thousand transaction records. Only food records were selected for the analysis. The partial objective of the paper was to examine the possibilities of data preparation such as restructuralization, logarithmic transformation, and dimensionality reduction. The main goal of the paper was to find meaningful patterns in the analyzed data and identify clusters of customers with similar behavior and needs. The segmentation was realized by various data mining techniques as follows: K-means clustering, Two Step clustering, and unsupervized algorithm based on neural networks called Self-Organizing Maps. The quality of results was evaluated by the Silhouette measure, which combines the principles of clusters separation and cohesion. After that the detailed analysis of the final segments was done.

Druh

D - Stať ve sborníku

CEP obor

BB - Aplikovaná statistika, operační výzkum

OECD FORD obor

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Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2011

Kód důvěrnosti údajů

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

Název statě ve sborníku

AGRARIAN PERSPECTIVES Proceedings of the 20th International Scientific Conference

ISBN

978-80-213-2196-0

ISSN

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e-ISSN

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Počet stran výsledku

6

Strana od-do

403-408

Název nakladatele

PEF ČZU v Praze

Místo vydání

Prague

Místo konání akce

Praha

Datum konání akce

13. 9. 2011

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000338036100043