Icon

03_​Analyze_​Clustering_​location_​data

Analyze Data by Training a k-Means Clustering on Location Data
Clustering: k-Means Read Data - The data contains various attributesabout the house and the house price Transform: - Filter the data - Normalize the data Evaluation Apply and score Model How to train Clustering Model? Step 1: Drag the k-Means node and double click to open the dialog Step 2: Select the "Number of clusters" as 3. In the "ColumnSelection"select the Columns "Lat" and "Long"Step 3: RIght Click on the node and select "Execute " to performclustering How to evaluate Clustering Model? Step 1: To visualize the clusters drag the Scatter Plot nodeand the OSM Map View. These nodes should be connectedto Color Manager to viulaize clusters with colors Step 2: To evaluate the clustering task, connect the clusteringoutput to "Silhouette Coefficient" node. Select the "ClusteringColumn Selection" as "Cluster"Step 3: Execute the node to get Silhouette Coefficients foreach instance, each cluster and for overall clustering task In CaliforniaVisualize the clusterson world mapColor accordingto clusterClusteringk=3Visualize the clustersEvaluate ClusterPerformance (higher value is preferred)Locations_dataStandardizethe dataDenormalize datato original values Row Filter OSM Map View Color Manager k-Means Scatter Plot SilhouetteCoefficient Table Reader Normalizer Denormalizer Clustering: k-Means Read Data - The data contains various attributesabout the house and the house price Transform: - Filter the data - Normalize the data Evaluation Apply and score Model How to train Clustering Model? Step 1: Drag the k-Means node and double click to open the dialog Step 2: Select the "Number of clusters" as 3. In the "ColumnSelection"select the Columns "Lat" and "Long"Step 3: RIght Click on the node and select "Execute " to performclustering How to evaluate Clustering Model? Step 1: To visualize the clusters drag the Scatter Plot nodeand the OSM Map View. These nodes should be connectedto Color Manager to viulaize clusters with colors Step 2: To evaluate the clustering task, connect the clusteringoutput to "Silhouette Coefficient" node. Select the "ClusteringColumn Selection" as "Cluster"Step 3: Execute the node to get Silhouette Coefficients foreach instance, each cluster and for overall clustering task In CaliforniaVisualize the clusterson world mapColor accordingto clusterClusteringk=3Visualize the clustersEvaluate ClusterPerformance (higher value is preferred)Locations_dataStandardizethe dataDenormalize datato original values Row Filter OSM Map View Color Manager k-Means Scatter Plot SilhouetteCoefficient Table Reader Normalizer Denormalizer

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: kathrinmelcher
Created at: 2019-12-13
On NodePit since: 2022-08-21
Last update: 2024-11-21
Created with KNIME version: v4.5.2
Tags: clusterk-MeansalgorithmclusteringBeginnerBeginner Cheat SheetBeginners Space

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

Try NodePit Runner!