A Recurrent Neural Network based Clustering Method for Binary Data Sets in Education
This is an incremental improvement for educators handling large student data sets.
The paper tackles the problem of clustering large binary S-P charts in education by proposing a recurrent neural network method that uses fixed points and basins of attraction, and confirms its effectiveness through experiments with an average caution index.
This paper studies an application of a recurrent neural network to clustering method for the S-P chart: a binary data set used widely in education. As the number of students increases, the S-P chart becomes hard to handle. In order to classify the large chart into smaller charts, we present a simple clustering method based on the network dynamics. In the method, the network has multiple fixed points and basins of attraction give clusters corresponding to small S-P charts. In order to evaluate the clustering performance, we present an important feature quantity: average caution index that characterizes singularity of students answer oatterns. Performing fundamental experiments, effectiveness of the method is confirmed.