2016
DOI: 10.3758/s13428-016-0780-7
|View full text |Cite
|
Sign up to set email alerts
|

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 … Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
1
1

Citation Types

0
10
0

Year Published

2018
2018
2024
2024

Publication Types

Select...
9

Relationship

2
7

Authors

Journals

citations
Cited by 20 publications
(10 citation statements)
references
References 38 publications
0
10
0
Order By: Relevance
“…Among alternative algorithms are k‐modes and k‐medians (relying on the mode and on the median, respectively); the output of these algorithms would be the same, all the variables at hand being dichotomous. Therefore, we used k‐modes . This approach, which is the application to the case of categorical data of the k‐means algorithm, aims at minimizing the Hamming distance between the centroid of a cluster and the patterns belonging to it .…”
Section: Methodsmentioning
confidence: 99%
“…Among alternative algorithms are k‐modes and k‐medians (relying on the mode and on the median, respectively); the output of these algorithms would be the same, all the variables at hand being dichotomous. Therefore, we used k‐modes . This approach, which is the application to the case of categorical data of the k‐means algorithm, aims at minimizing the Hamming distance between the centroid of a cluster and the patterns belonging to it .…”
Section: Methodsmentioning
confidence: 99%
“…Knowledge Space Theory [8][9] offers multiple methods for response pattern analysis to infer such prerequisite dependencies [18][19][20]. For example, [39] introduced the k-state procedure, a data-driven approach for building knowledge structures based on the k-modes clustering used in the area of data-mining. This clustering approach can be potentially used on big data offered by the Math Garden to explore prerequisite dependencies.…”
Section: Future Workmentioning
confidence: 99%
“…The third class of methods is data-driven construction of a knowledge structure (Chiusole et al, 2017;Falmagne et al, 2013;Robusto and Stefanutti, 2014;Sargin &Ünlü, 2009;Schrepp, 1999aSchrepp, , b, 2003Spoto et al, 2016;Villano, 1991). This last group of methods has recently received attention and it relies on the extraction of knowledge structures from data.…”
Section: Introductionmentioning
confidence: 99%
“…In Robusto and Stefanutti (2014) and Schrepp (1999a), only observed response patterns can be states of the constructed structure. On the other side, the k-states approach proposed by Chiusole et al (2017) can construct a knowledge structure by neither imposing restrictions on it, nor assuming that only observed patterns can be states. The k-states procedure is an incremental extension of the k-modes algorithm (Chaturvedi et al, 2001;Huang, 1999) to knowledge structure extraction.…”
Section: Introductionmentioning
confidence: 99%