Nearest Neighbor Approaches: Memory-Based Reasoning and Collaborative Filtering
You hear someone speak and immediately guess that she is from Australia. Why? Because her accent reminds you of other Australians you have met. Or you try a new restaurant expecting to like it because a friend with good taste recommended it. Both of these are examples of decisions based on experience. When faced with new situations, people are guided by memories of similar situations that they have experienced in the past. That is the basis for the data mining techniques introduced in this chapter.
Nearest-neighbor techniques are based on the concept of similarity. Memory-based reasoning (MBR) results are based on analogous situations in the past — much like deciding that a new friend is Australian based on past examples of Australian accents. Collaborative filtering (also called social information filtering) adds more information, using not just the similarities among neighbors, but also their preferences. The restaurant recommendation is an example of collaborative filtering.
Central to all these techniques is the idea of similarity. What really makes situations in the past similar to a new situation? Along with finding the similar records, there is the challenge of combining the information from the neighbors. These are the two key concepts for nearest-neighbor approaches.
This chapter begins with an introduction to MBR and an explanation of how it works. Because measures of distance and ...