摘要
基于现有的数据挖掘技术,采用优化初始聚类中心的方法来改进k-means聚类算法,对新疆农业大学学生一卡通消费数据进行研究和分析,为相关部门提供决策支持.首先,根据需求分析,选取部分学生于2014-2015学年在一卡通系统中产生的真实数据作为分析数据,并进行数据预处理,同时选择食堂消费次数和金额、超市消费次数和金额、就餐场所为特征属性;其次,使用改进的聚类算法进行分析,并且对比分析了基于三种距离度量方式下的k-means聚类算法;然后,得出分析结论,学生的食堂消费行为和超市消费行为;最后,探讨了如何根据分析所得结论为学校提供决策支持.
Based on the existing data mining technology, this article adopts the method of optimizing the initial clustering center to improve the k-means clustering algorithm, we can study of Xinjiang agricultural University student id card consumption data for the research and analysis,and provide decision support for the related departments.First of all, according to the demand analysis, we will choose some students for school year 2014-2015 real data in one cartoon system as data analysis,and data preprocessing, at the same time, we will choose the dining room number and amount, the supermarket consumption number and amount, the dining place for experimental characteristic attributes;Secondly, we use the improved clustering algorithm to analyze the data, and comparative analysis based on three kinds of distance measure under the k-means clustering algorithm;Then, the analysis conclusion, the student canteen consumption behavior and supermarket consumption behavior;Finally, the study was based on the conclusions of analysis provides decision support for schools.
出处
《计算机系统应用》
2017年第6期232-237,共6页
Computer Systems & Applications
基金
校前期资助课题(XJAU201426)
自治区自然科学基金(2014211B023)
关键词
优化k-means算法
特征属性
消费行为
距离度量方式
决策支持
optimization of the k-means clustering algorithm
feature properties
consumer behavior
distance measure
decision support
