- Grouping objects by similarity using k-means
- K-means clustering using scikit-learn
- A smarter way of placing the initial cluster centroids using k-means++
- Hard versus soft clustering
- Using the elbow method to find the optimal number of clusters
- Quantifying the quality of clustering via silhouette plots
- Organizing clusters as a hierarchical tree
- Grouping clusters in bottom-up fashion
- Performing hierarchical clustering on a distance matrix
- Attaching dendrograms to a heat map
- Applying agglomerative clustering via scikit-learn
- Locating regions of high density via DBSCAN
- Summary
Please refer to the README.md file in ../ch01 for more information about running the code examples.