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02_​kMeans

Clustering with k-Means

This workflow performs clustering of the iris dataset using k-Means.
Two workflows: one to build the k-Means prototypes (top) and one to apply them to new data (bottom).

This workflow performs clustering of the iris dataset using k-Means.Two workflows: one to build the k-Means prototypes (top) and one toapply them to new data (bottom). Building and saving k-Means prototypes Assigning cluster labels by the closest k-Means prototype Group intothree clustersAssign colorsto classesAssign shapeto clustersZ-scorenormalizationCluster new dataDenormalizewriting normalization modelwriting k-Means modelread k-MeansmodelreadnormalizationmodelLoad irisdata: trainingLoad irisdata: test k-Means Color Manager Shape Manager Normalizer Cluster Assigner Normalizer (Apply) Denormalizer Scatter Plot(JavaScript) Model Writer PMML Writer PMML Reader Model Reader Table Reader Table Reader This workflow performs clustering of the iris dataset using k-Means.Two workflows: one to build the k-Means prototypes (top) and one toapply them to new data (bottom). Building and saving k-Means prototypes Assigning cluster labels by the closest k-Means prototype Group intothree clustersAssign colorsto classesAssign shapeto clustersZ-scorenormalizationCluster new dataDenormalizewriting normalization modelwriting k-Means modelread k-MeansmodelreadnormalizationmodelLoad irisdata: trainingLoad irisdata: test k-Means Color Manager Shape Manager Normalizer Cluster Assigner Normalizer (Apply) Denormalizer Scatter Plot(JavaScript) Model Writer PMML Writer PMML Reader Model Reader Table Reader Table Reader

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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: Adrian Nembach
Created at: 2016-05-19
On NodePit since: 2024-04-10
Last update: 2024-04-30
Created with KNIME version: v5.2.3
Tags: clusteringk-Meansmachine learningdata miningTheGuideBookGIDS

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