Player Classification Using a Meta-Clustering Approach

Abstract

Player classification has recently become a key aspect of game design in areas such as adaptive game systems, player behaviour prediction, player tutoring and non-player character design. Past research has focused on the design of hierarchical, preferencebased and probabilistic models aimed at modelling players' behaviour. We propose a meta-classification approach that breaks the clustering of gameplay mixed data into three levels of analysis. The first level uses dimensionality reduction and partitional clustering of aggregate game data in an action/skillbased classification. The second level applies similarity-based clustering of action sequences to group players according to their preferences. For this we propose a new approach which uses Rubner's Earth Mover's Distance (EMD) as a similarity metric to compare histograms of players' game world explorations. The third level applies a combination of social network analysis metrics, such as shortest path length, to social data to find clusters in the players' social network. We test our approach in a gameplay dataset from a freely available first-person social hunting game.

Rough possibilistic meta-clustering of retail datasets

“…The results the set produced are continuously re-clustered until the final partitions are stable. We can cite the application of the meta-clustering process in [4], [5], and [6].…”

“…The meta-clustering, studied in numerous works [4], [5], [6], [7], [8], generally refers to a double clustering (or bi-clustering) where the results of the initial clustering of a set are used to improve the clustering of another set. The two sets depend on each other.…”

Video game personalisation techniques: A comprehensive survey

“…Anagnostou and Maragoudakis [70] cluster players of an actionarcade game using the Clustering Using REpresentatives (CURE) algorithm. Ramirez-Cano et al [71] cluster players of an action-hunting game using a three-level ''meta-clustering'' form of analysis. Gower et al [72] describe players of two different action games using multi-class Linear Discriminant Analysis (LDA).…”

Meta-clustering approach using possibilistic membership: Application to retail datasets

“…It uses the clustering results of a dataset to improve the clustering of another dataset. The use of the double-clustering in [11], [12], and [13] offers the possibility to improve the structure of each cluster. The meta-clustering means the clustering of clustering.…”

“…It uses the clustering results of one set to cluster another set of data. Many meta-clustering approaches were proposed including [11], [12], [13]. In [12], Lingras et al proposed a meta-clustering approach based on k-means method and applied real-world retail datasets.…”