Adjust parameters, fit the model, assign cluster labels, and visualize clusters through plots or dendrograms. Evaluate cluster quality with metrics like silhouette score.

The code above visualizes the clustering results with a scatter plot. Each data point is assigned a color based on its cluster, and the centroids are marked with a red 'X'.

Unsupervised Machine Learning problems involve clustering, adding samples into groups based on some measure of similarity because no labeled training data is available. There are many algorithms for ...

First, we systematically construct and label a large, publicly available scatterplot dataset. Second, we carry out a qualitative analysis based on the dataset and summarize the influence of visual ...

Use scatter plots, heatmaps, or dendrograms to illustrate the relationships between data points within each cluster. Visualization aids in interpreting the cluster structure and identifying patterns.

First, we systematically construct and label a large, publicly available scatterplot dataset. Second, we carry out a qualitative analysis based on the dataset and summarize the influence of visual ...