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Clothing_​customers

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: clothing_customersProprietà: PacktPublishinghttps://github.com/PacktPublishing/Data-Science-for-Marketing-Analytics-Second-Edition/blob/master/Chapter04/Datasets/Clothing_Customers.csvLicenza: MIT License Clothing_customersNormalizzazioneClusteringde-normalizzazioneStatisticsdispersione(calcolo cluster)medieClusterriduzionedella dimensionalitàNode 28 CSV Reader Normalizer k-Means Denormalizer Statistics Scatter Plot Color Manager Bar Chart PCA Linear Correlation dataset: clothing_customersProprietà: PacktPublishinghttps://github.com/PacktPublishing/Data-Science-for-Marketing-Analytics-Second-Edition/blob/master/Chapter04/Datasets/Clothing_Customers.csvLicenza: MIT License Clothing_customersNormalizzazioneClusteringde-normalizzazioneStatisticsdispersione(calcolo cluster)medieClusterriduzionedella dimensionalitàNode 28 CSV Reader Normalizer k-Means Denormalizer Statistics Scatter Plot Color Manager Bar Chart PCA Linear Correlation

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Download

To use this workflow in KNIME, download it from the below URL and open it in KNIME:

Download Workflow
Created by: Francesco Alaimo
Created at: 2022-08-19
On NodePit since: 2023-03-12
Last update: 2024-04-28
Created with KNIME version: v4.6.1
Tags: ClusteringSilhouette
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