Skill set profile clustering: the empty K-means algorithm with automatic specification of starting cluster centers
Rebecca Nugent, Nema Dean, Elizabeth Ayers · ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam) · 2010
Abstract. 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 2 K 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 k-means 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. 1