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Clustering_​Solution

Clustering - Solution
Exercise: ClusteringIn this exercise we will use the k-Means algorithm to cluster location data.1) Read the dataset location_data.table2) Filter to entries from California (region_code = CA)3) Train a k-means model with k=3. Use only position data for clustering (latitude and longitude)4) Calculate the Silhoutte Coefficients using the Silhouette Coefficient node5) Plot latitude and longitude in a view (OSM Map or Scatter Plot) and use that to help you visually optimize k In Californialocation_datak=3Evaluate ClusterPerformanceEvaluate ClusterPerformancelocation_dataIn CaliforniaRow Filter MISSING OSMMap View Color Manager Table Reader(deprecated) k-Means Scatter Plot SilhouetteCoefficient SilhouetteCoefficient Table Reader(deprecated) k-Means MISSING OSMMap View Scatter Plot Row Filter Color Manager Exercise: ClusteringIn this exercise we will use the k-Means algorithm to cluster location data.1) Read the dataset location_data.table2) Filter to entries from California (region_code = CA)3) Train a k-means model with k=3. Use only position data for clustering (latitude and longitude)4) Calculate the Silhoutte Coefficients using the Silhouette Coefficient node5) Plot latitude and longitude in a view (OSM Map or Scatter Plot) and use that to help you visually optimize k In Californialocation_datak=3Evaluate ClusterPerformanceEvaluate ClusterPerformancelocation_dataIn CaliforniaRow Filter MISSING OSMMap View Color Manager Table Reader(deprecated) k-Means Scatter Plot SilhouetteCoefficient SilhouetteCoefficient Table Reader(deprecated) k-Means MISSING OSMMap View Scatter Plot Row Filter Color Manager

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Created by: kathrinmelcher
Created at: 2019-12-13
On NodePit since: 2022-10-26
Last update: 2024-05-01
Created with KNIME version: v4.6.3
Tags: clusterk-MeansalgorithmeducationGIDSacademiaexerciseTheGuideBook

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