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Clothing_​customers(Elbow)

Determinazione del numero di cluster ottimale col metodo di Elbow

Questo workflow permette di valutare il numero di cluster ottimale per l'algoritmo K-means, utilizzando il metodo di Elbow


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-normalizzazioneStatisticsriduzionedella dimensionalitàElbowkLoop da 1 a 15Show ElbowNode 776Node 777 CSV Reader Normalizer k-Means Denormalizer Statistics Color Manager PCA Calculate sum ofsquared errors Java Edit Variable Find Elbow Counting Loop Start Loop End Variable toTable Column Line Plot (Labs) Scatter Plot (Labs) Bar Chart (Labs) 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-normalizzazioneStatisticsriduzionedella dimensionalitàElbowkLoop da 1 a 15Show ElbowNode 776Node 777 CSV Reader Normalizer k-Means Denormalizer Statistics Color Manager PCA Calculate sum ofsquared errors Java Edit Variable Find Elbow Counting Loop Start Loop End Variable toTable Column Line Plot (Labs) Scatter Plot (Labs) Bar Chart (Labs)

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

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Created by: Francesco Alaimo
Created at: 2022-08-19
On NodePit since: 2023-03-12
Last update: 2024-04-28
Created with KNIME version: v4.7.1
Tags: ClusteringElbow
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