Chapter 10: Working with Unlabeled Data – Clustering Analysis

Chapter Outline

• 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.