Cluster analysis plays an important role in exploratory data analysis, data preprocessing, and unsupervised learning tasks. Here, we'll find two notebooks: Cluster Analysis.ipynb and Silhouette Analysis.ipynb
Cluster Analysis.ipynb addresses questions like:
- How to perform cluster analysis using the K-Means technique?
- How to find the optimal number of clusters?
- How to identify appropriate features?
- Why and when do we need standardize the data?
- Which are the pros and cons of using K-Means?
- How to interpret the results?
Silhouette Analysis.ipynb talks about alternative ways of choosing the optimal number of clusters for the K-Means algorithm. More specifically, it shows how to perform silhouette analysis and plot the decision boundaries of K-Means for 2-dimensional data.