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ApplicationToyData_​UnlabeledTruth

Clustering on simulated clustered data

Clustering algorithms applied to simulated clustered data with 6 clusters

Original data K-means Hierarchical clustering DBSCAN Readingexample clusterdataK-meansclustering withk=6Normalize numerical attributesDe-normalizeback to theoriginal scaleAssign colors todifferent clustersScatter plotwith clustersfrom K-meansAverage linkagek=6De-normalizeback to theoriginal scaleAssign colors todifferent clustersScatter plotwith clustersfrom hierarchicalEpsilon=0.05MinPts=3De-normalizeback to theoriginal scaleAssign colors todifferent clustersScatter plotwith clustersfrom DBSCANDistancecalculationScatter plotwithout clusterlabels Table Reader k-Means Normalizer Denormalizer Color Manager Scatter Plot HierarchicalClustering Denormalizer Color Manager Scatter Plot DBSCAN Denormalizer Color Manager Scatter Plot Numeric Distances Scatter Plot Original data K-means Hierarchical clustering DBSCAN Readingexample clusterdataK-meansclustering withk=6Normalize numerical attributesDe-normalizeback to theoriginal scaleAssign colors todifferent clustersScatter plotwith clustersfrom K-meansAverage linkagek=6De-normalizeback to theoriginal scaleAssign colors todifferent clustersScatter plotwith clustersfrom hierarchicalEpsilon=0.05MinPts=3De-normalizeback to theoriginal scaleAssign colors todifferent clustersScatter plotwith clustersfrom DBSCANDistancecalculationScatter plotwithout clusterlabelsTable Reader k-Means Normalizer Denormalizer Color Manager Scatter Plot HierarchicalClustering Denormalizer Color Manager Scatter Plot DBSCAN Denormalizer Color Manager Scatter Plot Numeric Distances Scatter Plot

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Created by: Satoru
Created at: 2021-05-14
On NodePit since: 2024-03-19
Last update: 2024-11-21
Created with KNIME version: v4.3.3
Tags: K-meansHierarchical clusteringDBSCAN

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