A class of k-modes algorithms for extracting knowledge structures from data |
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Authors: | Debora de Chiusole Luca Stefanutti Andrea Spoto |
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Affiliation: | 1.FISPPA Department,University of Padua,Padova,Italy;2.Department of General Psychology,University of Padua,Padova,Italy |
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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. |
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