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Task 2 and 3

Model Evaluaton Dataset with No Normalisation Applied Data Collection Reading the PreprocessedNormalised Dataset from Task 1 Reading the Preprocessed NotNormalised Dataset from Task 1 Model Implementation: K-Means Dataset with Normalisation Applied Dataset with No Normalisation Applied Hyperparameter Tuning: Number of Clusters Hyperparameter Tuning: Number of Clusters Dataset with Normalisation Applied Visualising the Results Cluster Validity by Entropy and Quality Visualising the Results Cluster Validity by Entropy and Quality Read customer.csvRead customer.csvCluster Validy Metrics: Entropy and QualityExtract RowsAdd different valuesof K to the tableEnd LoopGet the columnsfor the tableFor normalised datasetK = 200 gives best Entropy and QualityListing various valuesfor number of clusters (K)Loop differentvalues of KImplementingK-Means ClusteringAlgorithmRead customer.csvAssign Colours to SubscribedApply DimensionalityReductionCluser Validity by Entropy & QualityPlace PCA0 and PCA1as first two columnsApply DimensionalityReductionPlace PCA0 and PCA1as first two columnsImplementingK-Means ClusteringAlgorithm (K=200)Assign Colours to SubscribedEnd LoopGet the columnsfor the tableFor unnormalised datasetK = 200 gives best Entropy and QualityListing various valuesfor number of clusters (K)Loop differentvalues of KImplementingK-Means ClusteringAlgorithmCluster Validy Metrics: Entropy and QualityExtract RowsAdd different valuesof K to the tableAfter K-Means ClusteringBefore K-Means ClusteringAfter K-Means ClusteringAssign Colours to SubscribedApply DimensionalityReductionCluser Validity by Entropy & QualityBefore K-Means ClusteringPlace PCA0 and PCA1as first two columnsApply DimensionalityReductionPlace PCA0 and PCA1as first two columnsImplementingK-Means ClusteringAlgorithm (K=200)Read customer.csvAssign Colours to SubscribedFile Reader File Reader Entropy Scorer Row Filter Variable toTable Column Loop End(deprecated) Column Resorter InteractiveTable (legacy) Table Creator Table Row toVariable Loop Start k-Means File Reader Color Manager PCA Entropy Scorer Column Resorter PCA Column Resorter k-Means Color Manager Loop End(deprecated) Column Resorter InteractiveTable (legacy) Table Creator Table Row toVariable Loop Start k-Means Entropy Scorer Row Filter Variable toTable Column Scatter Plot(JavaScript) Scatter Plot(JavaScript) Scatter Plot(JavaScript) Color Manager PCA Entropy Scorer Scatter Plot(JavaScript) Column Resorter PCA Column Resorter k-Means File Reader Color Manager Model Evaluaton Dataset with No Normalisation Applied Data Collection Reading the PreprocessedNormalised Dataset from Task 1 Reading the Preprocessed NotNormalised Dataset from Task 1 Model Implementation: K-Means Dataset with Normalisation Applied Dataset with No Normalisation Applied Hyperparameter Tuning: Number of Clusters Hyperparameter Tuning: Number of Clusters Dataset with Normalisation Applied Visualising the Results Cluster Validity by Entropy and Quality Visualising the Results Cluster Validity by Entropy and Quality Read customer.csvRead customer.csvCluster Validy Metrics: Entropy and QualityExtract RowsAdd different valuesof K to the tableEnd LoopGet the columnsfor the tableFor normalised datasetK = 200 gives best Entropy and QualityListing various valuesfor number of clusters (K)Loop differentvalues of KImplementingK-Means ClusteringAlgorithmRead customer.csvAssign Colours to SubscribedApply DimensionalityReductionCluser Validity by Entropy & QualityPlace PCA0 and PCA1as first two columnsApply DimensionalityReductionPlace PCA0 and PCA1as first two columnsImplementingK-Means ClusteringAlgorithm (K=200)Assign Colours to SubscribedEnd LoopGet the columnsfor the tableFor unnormalised datasetK = 200 gives best Entropy and QualityListing various valuesfor number of clusters (K)Loop differentvalues of KImplementingK-Means ClusteringAlgorithmCluster Validy Metrics: Entropy and QualityExtract RowsAdd different valuesof K to the tableAfter K-Means ClusteringBefore K-Means ClusteringAfter K-Means ClusteringAssign Colours to SubscribedApply DimensionalityReductionCluser Validity by Entropy & QualityBefore K-Means ClusteringPlace PCA0 and PCA1as first two columnsApply DimensionalityReductionPlace PCA0 and PCA1as first two columnsImplementingK-Means ClusteringAlgorithm (K=200)Read customer.csvAssign Colours to SubscribedFile Reader File Reader Entropy Scorer Row Filter Variable toTable Column Loop End(deprecated) Column Resorter InteractiveTable (legacy) Table Creator Table Row toVariable Loop Start k-Means File Reader Color Manager PCA Entropy Scorer Column Resorter PCA Column Resorter k-Means Color Manager Loop End(deprecated) Column Resorter InteractiveTable (legacy) Table Creator Table Row toVariable Loop Start k-Means Entropy Scorer Row Filter Variable toTable Column Scatter Plot(JavaScript) Scatter Plot(JavaScript) Scatter Plot(JavaScript) Color Manager PCA Entropy Scorer Scatter Plot(JavaScript) Column Resorter PCA Column Resorter k-Means File Reader Color Manager

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Created by: kagik
Created at: 2023-03-26
On NodePit since: 2024-03-29
Last update: 2024-11-21
Created with KNIME version: v5.2.1

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