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05-k-means-wine-template

Input Data Preparation TODO: Clustering with k-means 3.1.3 External Evaluation with Entropy Scorer Statistics Clustering Loop over different k 3.1.2 Internal Evaluation with Silhouette combine Data 3.1.2 find best k for k means based on silhouette coefficient and with parameter optimization loop find best k based on silhouette coeff. by means of a component 3.1.1 Internal Evaluation with Total Squared Distance 3.2 clustering with k-medoid TODO:Insert Node TODO:Insert Node TODO:Insert Node TODO:Insert Node TODO:Insert Node TODO:Insert Node Optional: Repeat clustering for best k and display silhouette TODO:Insert Node TODO:Insert Node TODO:Insert Node TODO:readwines.csv TODO:read wineslabels Normalize columnsShow statisticsExtract QualityCreate possible values for kNode 45Node 46Node 51Node 54Node 55combine internalvalidationsinto 1 tableExtract Qualitycombine internaland external validationsinto 1 tableNode 67Node 68Node 71labels(contain the "real" groups)winesNode 74loop over k from 2...10Node 80score with reference clusteringExtract QualityExtract QualityNode 86Node 87do the clustering for best kNode 90Node 93Node 94Node 95create a loop for kNode 98Node 99Node 100Node 101Node 102SummarizeNode 104Normalizer Statistics Row Filter Table Creator Rank Correlation Linear Correlation Color Manager Shape Manager Scatter Plot(local) Joiner (deprecated) Row Filter Joiner (deprecated) Column Resorter Sorter Number To String CSV Reader CSV Reader Normalizer Parameter OptimizationLoop Start Number To String Entropy Scorer Row Filter Row Filter ParameterOptimization Loop End Table Columnto Variable k-Means 2D/3D Scatterplot Variable toTable Column Variable toTable Column Variable toTable Column Table Row ToVariable Loop Start Sorter SilhouetteCoefficient Math Formula Bar Chart Number To String Loop End Joiner Input Data Preparation TODO: Clustering with k-means 3.1.3 External Evaluation with Entropy Scorer Statistics Clustering Loop over different k 3.1.2 Internal Evaluation with Silhouette combine Data 3.1.2 find best k for k means based on silhouette coefficient and with parameter optimization loop find best k based on silhouette coeff. by means of a component 3.1.1 Internal Evaluation with Total Squared Distance 3.2 clustering with k-medoid TODO:Insert Node TODO:Insert Node TODO:Insert Node TODO:Insert Node TODO:Insert Node TODO:Insert Node Optional: Repeat clustering for best k and display silhouette TODO:Insert Node TODO:Insert Node TODO:Insert Node TODO:readwines.csv TODO:read wineslabels Normalize columnsShow statisticsExtract QualityCreate possible values for kNode 45Node 46Node 51Node 54Node 55combine internalvalidationsinto 1 tableExtract Qualitycombine internaland external validationsinto 1 tableNode 67Node 68Node 71labels(contain the "real" groups)winesNode 74loop over k from 2...10Node 80score with reference clusteringExtract QualityExtract QualityNode 86Node 87do the clustering for best kNode 90Node 93Node 94Node 95create a loop for kNode 98Node 99Node 100Node 101Node 102SummarizeNode 104Normalizer Statistics Row Filter Table Creator Rank Correlation Linear Correlation Color Manager Shape Manager Scatter Plot(local) Joiner (deprecated) Row Filter Joiner (deprecated) Column Resorter Sorter Number To String CSV Reader CSV Reader Normalizer Parameter OptimizationLoop Start Number To String Entropy Scorer Row Filter Row Filter ParameterOptimization Loop End Table Columnto Variable k-Means 2D/3D Scatterplot Variable toTable Column Variable toTable Column Variable toTable Column Table Row ToVariable Loop Start Sorter SilhouetteCoefficient Math Formula Bar Chart Number To String Loop End Joiner

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Created by: tlauer1
Created at: 2018-06-08
On NodePit since: 2021-06-16
Last update: 2024-12-23
Created with KNIME version: v4.5.1

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