Current Slide

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