This paper discusses the relation between dorm arrangement and student performance. One of the unsupervised learning algorithms, k-means algorithm, is mainly used in the process of analysis. Students are clustered into several clusters according to their similarity of performance scores. This paper analyzes the result of clustering by comparing it with actual dorm arrangement. In the end, drawbacks of k-means and reliability of this student dorm-performance relation are evaluated. Finally, this paper draws a conclusion that student performances are influenced by dorm arrangement.
['Man Li', 'Ruisheng Shi']
Mining the relation between dorm arrangement and student performance