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Session - IRIS Classification & Prediction - Basic1

Clustering on the Iris data

Clustering algorithms applied to simulated clustered data with 6 clusters

True clusters K-means Hierarchical clustering DBSCAN https://en.wikipedia.org/wiki/Receiver_operating_characteristic Supervised Learning Unsupervised Learning Exercise: Add A metanode with Decision Tree learner Excercise: Add ROC curve for eachmodel and each Species Exercise: Add to each of the clustering methods a Silouette Metric node andvisualization (see how it is done in the Self Organizing Map Metanode Readingthe irisdataK-meansclustering withk=3Normalize numerical attributesDe-normalizeback to theoriginal scaleAssign colors todifferent clustersScatter plotwith clustersfrom K-meansAverage linkagek=3De-normalizeback to theoriginal scaleAssign colors todifferent clustersScatter plotwith clustersfrom hierarchicalEpsilon=0.25MinPts=3De-normalizeback to theoriginal scaleAssign colors todifferent clustersScatter plotwith clustersfrom DBSCANDistancecalculationScatter plotwithout labelsNode 155IRIS.csvNode 157Node 160Node 161Node 163Node 164High Level SummaryNode 167Node 171Node 853 Table Reader(deprecated) k-Means Normalizer Denormalizer Color Manager Scatter Plot HierarchicalClustering Denormalizer Color Manager Scatter Plot DBSCAN Denormalizer Color Manager Scatter Plot Numeric Distances Scatter Plot CSV Reader Data Preparations SVM Fuzzy Rules ConjunctiveRule(3.7) GroupBy Create a SimpleData Driven Rule PythonVisaulization Self Organizing Map True clusters K-means Hierarchical clustering DBSCAN https://en.wikipedia.org/wiki/Receiver_operating_characteristic Supervised Learning Unsupervised Learning Exercise: Add A metanode with Decision Tree learner Excercise: Add ROC curve for eachmodel and each Species Exercise: Add to each of the clustering methods a Silouette Metric node andvisualization (see how it is done in the Self Organizing Map Metanode Readingthe irisdataK-meansclustering withk=3Normalize numerical attributesDe-normalizeback to theoriginal scaleAssign colors todifferent clustersScatter plotwith clustersfrom K-meansAverage linkagek=3De-normalizeback to theoriginal scaleAssign colors todifferent clustersScatter plotwith clustersfrom hierarchicalEpsilon=0.25MinPts=3De-normalizeback to theoriginal scaleAssign colors todifferent clustersScatter plotwith clustersfrom DBSCANDistancecalculationScatter plotwithout labelsNode 155IRIS.csvNode 157Node 160Node 161Node 163Node 164High Level SummaryNode 167Node 171Node 853 Table Reader(deprecated) k-Means Normalizer Denormalizer Color Manager Scatter Plot HierarchicalClustering Denormalizer Color Manager Scatter Plot DBSCAN Denormalizer Color Manager Scatter Plot Numeric Distances Scatter Plot CSV Reader Data Preparations SVM Fuzzy Rules ConjunctiveRule(3.7) GroupBy Create a SimpleData Driven Rule PythonVisaulization Self Organizing Map

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Created by: Satoru
Created at: 2021-05-14
On NodePit since: 2024-03-26
Last update: 2024-04-27
Created with KNIME version: v4.5.1
Tags: K-meansHierarchical clusteringDBSCAN

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