Icon

01_​Clustering_​Algorithms_​Exercise

Implementation of Different Clustering Algorithms

This workflow clusters the iris dataset using Hierarchical Clustering, DBSCAN and k-means



Hierarchical Clustering DBSCAN k-Means Assign colorsto classesLoad irisdataZ-scorenormalizationDenormalizeHierarchical ClusteringDBSCANK-meansDenormalizeAssign colorsto classesDenormalizeAssign colorsto classesAssign colorsto classesTrue classes Color Manager Table Reader Normalizer Denormalizer Scatter Plot Scatter Plot Scatter Plot Denormalizer Color Manager Denormalizer Color Manager Color Manager Scatter Plot Hierarchical Clustering DBSCAN k-Means Assign colorsto classesLoad irisdataZ-scorenormalizationDenormalizeHierarchical ClusteringDBSCANK-meansDenormalizeAssign colorsto classesDenormalizeAssign colorsto classesAssign colorsto classesTrue classes Color Manager Table Reader Normalizer Denormalizer Scatter Plot Scatter Plot Scatter Plot Denormalizer Color Manager Denormalizer Color Manager Color Manager Scatter Plot

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: 2021-08-06
Last update: 2024-11-21
Created with KNIME version: v4.3.1
Tags: clusteringmachine learningdata mininghierarchical clusteringTheGuideBookGIDSUni Berlin

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

Try NodePit Runner!