title: A class of k-modes algorithms for extracting knowledge structures from data.
abstract: One of the most crucial issues in knowledge space theory is the construction of the so-called knowledge structures. In the present paper, a new data-driven procedure for large data sets is described, which overcomes some of the drawbacks of the already existing methods. The procedure, called k-states, is an incremental extension of the k-modes algorithm, which generates a sequence of locally optimal knowledge structures of increasing size, among which a "best" model is selected. The performance of k-states is compared to other two procedures in both a simulation study and an empirical application. In the former, k-states displays a better accuracy in reconstructing knowledge structures; in the latter, the structure extracted by k-states obtained a better fit.
authors: FISPPA Department, University of Padua, Padova, Italy.
authors: Debora
authors: D
authors: de Chiusole
authors: FISPPA Department, University of Padua, Padova, Italy.
authors: Luca
authors: L
authors: Stefanutti
authors: Department of General Psychology, University of Padua, Via Venezia 8, 35131, Padova, PD, Italy. andrea.spoto@unipd.it.
authors: Andrea
authors: A
authors: Spoto
mesh_terms: D000465:Algorithms; D016208:Databases, Factual; D006801:Humans; D019359:Knowledge; D011582:Psychological Theory
keywords: Data-driven procedures; Knowledge structures; k-modes