An improved k-means clustering approach for teaching evaluation

Abstract

Intelligent evaluation as an important branch in the field of artificial intelligence is a decision-making process of simulating the domain experts to solve complex problems. In this paper, we put forward a kind of intelligent evaluation method based on clustering, which can be used to mine different groups of teachers and evaluate the teaching quality automatically. Clustering analysis method is one of the main analytical methods in data mining, which influences the clustering results directly. In this paper Firstly, we do some improvement on traditional K-means clustering due to its shortcomings. Secondly, we propose a model or teaching quality evaluation based on improved K-means clustering. © 2011 Springer-Verlag Berlin Heidelberg.