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Description
| - 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.
- 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. (en)
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Title
| - Application of data-mining techniques in customer segmentation
- Application of data-mining techniques in customer segmentation (en)
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skos:prefLabel
| - Application of data-mining techniques in customer segmentation
- Application of data-mining techniques in customer segmentation (en)
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skos:notation
| - RIV/60460709:41110/11:51189!RIV15-MSM-41110___
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http://linked.open...avai/riv/aktivita
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http://linked.open...avai/riv/aktivity
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http://linked.open...vai/riv/dodaniDat
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http://linked.open...aciTvurceVysledku
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http://linked.open.../riv/druhVysledku
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http://linked.open...iv/duvernostUdaju
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http://linked.open...titaPredkladatele
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http://linked.open...dnocenehoVysledku
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http://linked.open...ai/riv/idVysledku
| - RIV/60460709:41110/11:51189
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http://linked.open...riv/jazykVysledku
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http://linked.open.../riv/klicovaSlova
| - Data mining, Clustering, Customer Segmentation, IBM SPSS Modeler (en)
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http://linked.open.../riv/klicoveSlovo
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http://linked.open...ontrolniKodProRIV
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http://linked.open...v/mistoKonaniAkce
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http://linked.open...i/riv/mistoVydani
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http://linked.open...i/riv/nazevZdroje
| - AGRARIAN PERSPECTIVES Proceedings of the 20th International Scientific Conference
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http://linked.open...in/vavai/riv/obor
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http://linked.open...ichTvurcuVysledku
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http://linked.open...cetTvurcuVysledku
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http://linked.open...UplatneniVysledku
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http://linked.open...iv/tvurceVysledku
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http://linked.open...vavai/riv/typAkce
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http://linked.open...ain/vavai/riv/wos
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http://linked.open.../riv/zahajeniAkce
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number of pages
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http://purl.org/ne...btex#hasPublisher
| - Česká zemědělská univerzita v Praze. Provozně ekonomická fakulta
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https://schema.org/isbn
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http://localhost/t...ganizacniJednotka
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