Icon

20210616 Pikairos K-Means followed by Hierarchical Cluster Asigner

K-Means followed by Hierarchical Clustering

This workflow clusters the iris dataset using K-Means + Hierarchical Clustering


Clustering the iris datasetusing K-Means Hierarchical Clustering Load irisdataZ-scorenormalizationInteractive view toselect the tresholdGroup intothree clusterscomplete linkageDistance matrix from K-Mean vectorsfor hierarchical clusteringeuclidean distancevisualizedendrogram21 K-Meansjust for thisdummy exampleJoin by RowID (1st input port)from HCA& Cluster Column (2nd input port)from K-meansAfter joiningyou get herefor every initial rowthe K-means clustering columnfollowed by the Hierarchical Tree Clustering columnWould you need to havethe initial data before normalizationthe use this 2nd optionJoin by RowID (1st input port)from HCA& Cluster Column (2nd input port)from K-meansConserve Initial RowIDin an extra columnfor later useConserve Initial RowIDin an extra columnfor later useAfter joiningyou get herefor every initial rowthe K-means clustering columnfollowed by the Hierarchical Tree Clustering columnTable Reader Normalizer HierarchicalCluster Assigner Hierarchical Clustering(DistMatrix) Distance MatrixCalculate HierarchicalCluster View k-Means Joiner InteractiveTable (local) Joiner RowID RowID InteractiveTable (local) Clustering the iris datasetusing K-Means Hierarchical Clustering Load irisdataZ-scorenormalizationInteractive view toselect the tresholdGroup intothree clusterscomplete linkageDistance matrix from K-Mean vectorsfor hierarchical clusteringeuclidean distancevisualizedendrogram21 K-Meansjust for thisdummy exampleJoin by RowID (1st input port)from HCA& Cluster Column (2nd input port)from K-meansAfter joiningyou get herefor every initial rowthe K-means clustering columnfollowed by the Hierarchical Tree Clustering columnWould you need to havethe initial data before normalizationthe use this 2nd optionJoin by RowID (1st input port)from HCA& Cluster Column (2nd input port)from K-meansConserve Initial RowIDin an extra columnfor later useConserve Initial RowIDin an extra columnfor later useAfter joiningyou get herefor every initial rowthe K-means clustering columnfollowed by the Hierarchical Tree Clustering columnTable Reader Normalizer HierarchicalCluster Assigner Hierarchical Clustering(DistMatrix) Distance MatrixCalculate HierarchicalCluster View k-Means Joiner InteractiveTable (local) Joiner RowID RowID InteractiveTable (local)

Nodes

Extensions

Links

Download

To use this workflow in KNIME, download it from the below URL and open it in KNIME:

Download Workflow
Created by: Adrian Nembach
Created at: 2016-05-19
On NodePit since: 2022-02-06
Last update: 2024-11-21
Created with KNIME version: v4.2.4
Tags: clusteringmachine learningdata mininghierarchical clusteringK-Means

Deploy, schedule, execute, and monitor your KNIME workflows locally, in the cloud or on-premises – with our brand new NodePit Runner.

Try NodePit Runner!