Clustering Methods in Game Data Science

Abstract

This chapter discusses different clustering methods and their application to game data. In particular, the chapter details K-means, Fuzzy C-Means, Hierarchical Clustering, Archetypical Analysis, and Model-based clustering techniques. It discusses the disadvantages and advantages of the different methods and discusses when you may use one method vs. the other. It also identifies and shows you ways to visualize the results to make sense of the resulting clusters. It also includes details on how one would evaluate such clusters or go about applying the algorithms to a game dataset. The chapter includes labs to delve deeper into the application of these algorithms on real game data.