Chapter 6. Similarity, Neighbors, and Clusters
Fundamental concepts: Calculating similarity of objects described by data; Using similarity for prediction; Clustering as similarity-based segmentation.
Exemplary techniques: Searching for similar entities; Nearest neighbor methods; Clustering methods; Distance metrics for calculating similarity.
Similarity underlies many data science methods and solutions to business problems. If two things (people, companies, products) are similar in some ways they often share other characteristics as well. Data mining procedures often are based on grouping things by similarity or searching for the “right” sort of similarity. We saw this implicitly in previous chapters where modeling procedures create boundaries for grouping instances together that have similar values for their target variables. In this chapter we will look at similarity directly, and show how it applies to a variety of different tasks. We include sections with some technical details, in order that the more mathematical reader can understand similarity in more depth; these sections can be skipped.
Different sorts of business tasks involve reasoning from similar examples:
- We may want to retrieve similar things directly. For example, IBM wants to find companies that are similar to their best business customers, in order to have the sales staff look at them as prospects. Hewlett-Packard maintains many high-performance servers for clients; this maintenance is aided by a tool that, given ...