An improved K-prototype-based cluster algorithm for mixed educational data mining
by Yuan Wang; Liping Yang; Jun Wu; Shaomiao Chen
International Journal of Embedded Systems (IJES), Vol. 15, No. 5, 2022

Abstract: Educational data mining is one of the important ways to improve the quality of education. Clustering is a classic data mining method. It is often applied to single-attribute data. Educational data which contains multiple types of attributes. This brings great challenges to the dissimilarity measurement of clustering algorithms. In this paper, we first collect a large amount of student education data with 18 different types of attributes. Then, based on the K-prototype algorithm framework, we improve the dissimilarity measurement method so that it can be applied to the mixed attribute characteristics of education data. Finally, a Kruskal-Wallis test is used to determine the significance of clustering properties. Experimental results show that the proposed method can obtain higher quality clustering than the state-of-the-art clustering methods.

Online publication date: Wed, 23-Nov-2022

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