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Clothing_​customers(Silhouette)

Determinazione del numero di cluster ottimale col punteggio Silhouette

Questo workflow permette di valutare il numero di cluster ottimale per l'algoritmo K-means, utilizzando il 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_customersNormalizzazionede-normalizzazioneStatisticsriduzionedella dimensionalitàPCAK-meancon cluster ottimaliFiltro colonneAssegno a clusterEstraggo un sample CSV Reader Normalizer Denormalizer Statistics PCA 3D ScatterPlot (Plotly) Color Manager k-Means PunteggioSilhouette Column Filter Mostra Clusters Cluster Assigner Row Sampling dataset: clothing_customersProprietà: PacktPublishinghttps://github.com/PacktPublishing/Data-Science-for-Marketing-Analytics-Second-Edition/blob/master/Chapter04/Datasets/Clothing_Customers.csvLicenza: MIT License Clothing_customersNormalizzazionede-normalizzazioneStatisticsriduzionedella dimensionalitàPCAK-meancon cluster ottimaliFiltro colonneAssegno a clusterEstraggo un sample CSV Reader Normalizer Denormalizer Statistics PCA 3D ScatterPlot (Plotly) Color Manager k-Means PunteggioSilhouette Column Filter Mostra Clusters Cluster Assigner Row Sampling

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

Download Workflow
Created by: falai
Created at: 2022-08-19
On NodePit since: 2023-05-23
Last update: 2024-12-17
Created with KNIME version: v4.7.3
Tags: ClusteringSilhouette

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