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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.