目前，我国高校都已经建立了较为全面的贫困大学生资助体系，但是由于学生的贫困生申请信息偏于主观、贫困指标难以量化等因素，使得贫困生认定工作仍然是高校资助决策中的难点问题。寻求一种客观、高效的贫困生认定评估标准，成为高校资助工作研究的重要内容。该文采用数据挖掘的手段，从学生的校园一卡通消费数据入手，使用K-means聚类算法对数据进行分析。在此基础上，该文建立了基于聚类结果的贫困生指数算法计算每个学生的贫困生指数，用于辅助高校资助决策工作。%At present, most of the universities and colleges in China have established a comprehensive system for aiding impover-ished students. However, two of the factors accounting for the fact that identifying poor students is still a difficult problem are that the poor students application information is somewhat subjective and that the degree of poverty is difficult to quantify. Seek-ing an objective and efficient evaluation criterion for identifying impoverished students is one of the most important research themes in college funding. In this paper, data mining tools such as the K-means clustering algorithm are used to analyze campus card consumption data. In addition, based on the clustering result, an impoverished students index algorithm for calculating each student’s poverty index is established, which assists in decision-making of college funding.
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