12 Clustering
This chapter covers
- Classifying different types of clustering
- Partitioning clustering
- Understanding and implementing k-means
- Density-based clustering
- Understanding and implementing DBSCAN
- OPTICS: Refining DBSCAN as hierarchical clustering
- Evaluating clustering results
In the previous chapters we have described, implemented, and applied three data structures designed to efficiently solve nearest neighbor search. When we moved to their applications, we mentioned that clustering was one of the main areas where an efficient nearest neighbor search could make a difference. We had to delay this discussion, but now it’s finally time to put the icing on the cake and get the most out of our hard work. In this chapter, we will first briefly ...
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