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Customer Segmentation

Determinazione del numero di cluster ottimale col punteggio Silhouette

Questo workflow permette di valutare il numero di cluster ottimale per l'algoritmo K-means, utilizzandoil punteggio Silhouette



dataset: Online retaildimension: 541909 recordsProprietà: UCIhttp://archive.ics.uci.edu/ml/datasets/Online+RetailLicenza: CC BY 4.0Citation DOI: Daqing Chen, Sai Liang Sain, and Kun Guo, Data mining for the online retailindustry: A case study of RFM model-based customer segmentation using data mining,Journal of Database Marketing and Customer Strategy Management, Vol. 19, No. 3, pp.197-208, 2012 (Published online before print: 27 August 2012. doi: 10.1057/dbm.2012.17). Leggo fileremotoStatistics Excel Reader Data Preparation Check Iniziale Segmentation Statistics (Labs) dataset: Online retaildimension: 541909 recordsProprietà: UCIhttp://archive.ics.uci.edu/ml/datasets/Online+RetailLicenza: CC BY 4.0Citation DOI: Daqing Chen, Sai Liang Sain, and Kun Guo, Data mining for the online retailindustry: A case study of RFM model-based customer segmentation using data mining,Journal of Database Marketing and Customer Strategy Management, Vol. 19, No. 3, pp.197-208, 2012 (Published online before print: 27 August 2012. doi: 10.1057/dbm.2012.17). Leggo fileremotoStatistics Excel Reader Data Preparation Check Iniziale Segmentation Statistics (Labs)

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Created by: Francesco Alaimo
Created at: 2022-08-19
On NodePit since: 2023-03-12
Last update: 2024-04-27
Created with KNIME version: v4.7.1
Tags: ClusteringSilhouette
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