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01_​Clustering_​solution

Clustering - solution
Clustering: k-Means Exercise: Clustering1) Filter to entries from California (region_code = CA) (Row Filter node)2) Train a k-Means model with k=3. Use only position data for clustering (latitude and longitude) (k-Means node)3) Calculate the Silhoutte Coefficients using the Silhouette Coefficient node4) Optional: Plot latitude and longitude in a view (OSM Map View node or Scatter Plot node) and use that tohelp you visually optimize k In CaliforniaEvaluate ClusterPerformanceLocations_data Row Filter OSM Map View Color Manager k-Means Scatter Plot SilhouetteCoefficient Table Reader Clustering: k-Means Exercise: Clustering1) Filter to entries from California (region_code = CA) (Row Filter node)2) Train a k-Means model with k=3. Use only position data for clustering (latitude and longitude) (k-Means node)3) Calculate the Silhoutte Coefficients using the Silhouette Coefficient node4) Optional: Plot latitude and longitude in a view (OSM Map View node or Scatter Plot node) and use that tohelp you visually optimize k In CaliforniaEvaluate ClusterPerformanceLocations_data Row Filter OSM Map View Color Manager k-Means Scatter Plot SilhouetteCoefficient Table Reader

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Created by: kathrinmelcher
Created at: 2019-12-13
On NodePit since: 2022-11-15
Last update: 2024-04-18
Created with KNIME version: v4.6.1
Tags: clusterk-Meansalgorithmeducation

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