摘要
校园一卡通系统储存了大量的数据信息,涉及学生校园生活与学习的各个方面,其中的学生消费数据最为稳定和完整。对传统FCM算法进行改进,得到改进后的基于密度权重的模糊C均值算法(DWFCM),并使用这两种方法分别对预处理后的一卡通数据进行挖掘分析,得到的结果表明DWFCM算法有更好的聚类效果,可以为学校困难学生认定和资助提供支撑数据,从而提高资助的精准性。
Campus card system stores a large amount of data and information,involving all aspects of students'campus life and learning,among which students'consumption data is the most stable and complete.This paper improves the traditional FCM algorithm and obtains the improved fuzzy C-means algorithm based on density weight(DWFCM).Using these two methods to mine and analyze the pre-processed card data,the results show that the DWFCM algorithm has better clustering effect.It can provide assistant supporting data for the identification and funding of school students with difficulties,so as to improve the accuracy of funding.
作者
钮永莉
冯胜安
贾红雯
Niu Yong-li;FENG Sheng-an;JIA Hong-wen(Department of Information Engineering,Chuzhou Vocational and Technical College,ChuZhou 239000,China)
出处
《信阳农林学院学报》
2019年第3期89-92,共4页
Journal of Xinyang Agriculture and Forestry University
基金
滁州职业技术学院校级科研重点项目(YJZ-2018-19)
2017年院级教研项目(2017zlgc044)
