#Data Visualization
This project is designed to leverage the KNIME Analytics Platform for data clustering and cluster validity assessment. It involves constructing KNIME workflows to understand data characteristics, preprocess the data, perform clustering, and evaluate the results using various statistical measures.
- To understand the data characteristics and quality.
- To preprocess the data for clustering analysis.
- To utilize clustering algorithms to segment data.
- To evaluate clustering results with cluster validity measures such as silhouette coefficients.
The project is divided into three main tasks:
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Data Understanding and Preprocessing: Development of a KNIME workflow to assess data quality, perform necessary preprocessing, and visualize the data for better understanding.
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Clustering: Utilizing clustering algorithms, such as k-means, to segment data into meaningful groups.
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Cluster Validity: Applying cluster validity measures, such as silhouette coefficients and entropy scores, to evaluate the results.
Instructions on how to set up the KNIME environment and import the workflow:
- Install KNIME Analytics Platform from KNIME Download Page.
- Open KNIME and choose
File > Import KNIME workflow...to import the provided workflow file.
To run the workflow:
- Open the KNIME workflow.
- Configure each node as needed or use the provided settings.
- Execute the nodes in sequence or run the entire workflow.
This project includes various visualizations to aid in the interpretation of the data and the clustering results:
- Scatter plots for pre and post clustering data representation.
- Box plots and histograms for distribution analysis.
The README could include a brief summary of key findings, such as:
- Clustering outcomes, represented visually and assessed statistically.
- Observations from silhouette coefficient analysis indicating the degree of separation between clusters.