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Summary

  • Cluster analysis groups objects based on their similarity and has wide applications
  • Measure of similarity can be computed for various types of data
  • Clustering algorithms can be categorized into partitioning methods, hierarchical methods, density-based methods, grid-based methods, and model-based methods
  • K-means and K-medoids algorithms are popular partitioning-based clustering algorithms
  • Birch and Chameleon are interesting hierarchical clustering algorithms, and there are also probabilistic hierarchical clustering algorithms
  • DBSCAN, OPTICS, and DENCLU are interesting density-based algorithms
  • STING and CLIQUE are grid-based methods, where CLIQUE is also a subspace clustering algorithm
  • Quality of clustering results can be evaluated in various ways

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