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CS3DS19: Construct a KNIME workflow to understand the data characteristics and quality, report: Data Science Algorithms and Tools Course Work, UOW, UK

Task 1: Data Understanding and Preprocessing (30%)
Construct a KNIME workflow to understand the data characteristics and quality, report and discuss your findings. Based on the data understanding, identify and discuss the required data preprocessing steps, and perform them in the KNIME workflow. Also visualize the data before clustering, with colors showing the class labels.

Task 2: Clustering (30%)
Next, add to or construct another KNIME workflow to build clustering model(s) for the dataset. You can use any clustering algorithm(s). In the report, discuss and justify your selection of algorithm(s) and parameters. You may use experiments to support your discussions and justifications. Also visualize the clustered data, with colors showing the cluster labels.

Task 3: Cluster Validity (20%)
Then, add to the KNIME workflow to evaluate your clustering results with appropriate cluster validity measures. In the report, discuss and justify your selection of measures, present and discuss the results.

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