{"ID":2881440,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.13224","arxiv_id":"2508.13224","title":"A Recurrent Neural Network based Clustering Method for Binary Data Sets in Education","abstract":"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.","short_abstract":"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 b...","url_abs":"https://arxiv.org/abs/2508.13224","url_pdf":"https://arxiv.org/pdf/2508.13224v1","authors":"[\"Mizuki Ohira\",\"Toshimichi Saito\"]","published":"2025-08-17T13:26:43Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.CY\"]","methods":"[]","has_code":false}