用循环神经网络对教育中的二值数据聚类，提升大样本分析效率。
A Recurrent Neural Network based Clustering Method for Binary Data Sets in Education
- 基于网络动力学的多稳态结构实现学生答题模式聚类。
- 提出平均谨慎指数衡量学生作答模式的异常程度。
- 适合处理大规模教育数据，尤其适用于复杂答题模式识别。
本文研究了将循环神经网络应用于教育中广泛使用的S-P图（二值数据集）的聚类方法。随着学生数量增加，S-P图变得难以处理。为将大图分解为多个小图，本文提出一种基于网络动力学的简单聚类方法：网络具有多个稳定状态，其吸引域对应于若干小型S-P图。为评估聚类效果，引入关键特征量——平均谨慎指数，用于表征学生作答模式的奇异程度。通过基础实验验证了该方法的有效性。
原文摘要 · Abstract (English)
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.
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