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Clustering_​Solution

Clustering - Solution

- Filter rows
- Train a k-Means model
- Visualize clustered entries on Scatter plot and OSM Map
- Calculate Silhouette Coefficients

URL: Guide to Intelligent Data Science https://www.datascienceguide.org/


Exercise: Clustering

In this exercise we will use the k-Means algorithm to cluster location data.

  1. Read the dataset location_data.table

  2. Filter to entries from California (region_code = CA)

  3. Train a k-means model with k=3. Use only position data for clustering (latitude and longitude)

  4. Calculate the Silhoutte Coefficients using the Silhouette Coefficient node

  5. Plot latitude and longitude in a view (OSM Map or Scatter Plot) and use that to help you visually optimize k

location_data
Table Reader
location_data
Table Reader
In California
Row Filter
k=3
k-Means
Color Manager
Evaluate ClusterPerformance
Silhouette Coefficient
OSM Map View
Evaluate ClusterPerformance
Silhouette Coefficient
k-Means
OSM Map View
Color Manager
Scatter Plot
In California
Row Filter
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: kathrinmelcher
Created at: 2019-12-13
On NodePit since: 2025-03-08
Last update: 2026-11-10
Created with KNIME version: v5.4.0
Tags: clusterk-MeansalgorithmeducationGIDSacademiaexerciseTheGuideBook

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