Skill Set Profile Clustering: The Empty K-Means Algorithm with Automatic Specification of Starting Cluster Centers

While students' skill set profiles can be estimated with formal cognitive diagnosis models [8], their computational complexity makes simpler proxy skill estimates attractive [1, 4, 6]. These estimates can be clustered to generate groups of similar students. Often hierarchical agglomerative clustering or k-means clustering is utilized, requiring, for K skills, the specification of 2K clusters. The number of skill set profiles/clusters can quickly become computationally intractable. Moreover, not all profiles may be present in the population. We present a flexible version of kmeans that allows for empty clusters. We also specify a method to determine efficient starting centers based on the Q-matrix. Combining the two substantially improves the clustering results and allows for analysis of data sets previously thought impossible.

• Type: Conference
• Year: 2010
• Where: EDM
• Authors: Rebecca Nugent, Nema Dean, Elizabeth Ayers
• Keywords: Data Mining, EDM 2010, Hierarchical Agglomerative Clustering, Proxy Skill Estimates, Skill Set Profiles