基于校园一卡通消费数据的几种聚类算法的分析比较

Analysis and Comparison of Several Clustering Algorithms Based on Campus Card Consumption Data

随着高校管理信息化的加速和高校管理部门对各类校园信息资源需求的不断加强,校园一卡通被广泛应用于学生生活的各个领域,并要求对其存储的海量数据进行挖掘分析为各个部门提供决策依据.聚类算法作为最常用的数据挖掘方法之一被广泛应用于一卡通数据挖掘,但目前不清楚哪种方法更适用于一卡通数据.使用多种常用聚类算法对一卡通数据进行了实验,得出了最适合挖掘该数据的聚类算法,并分析了相关原因.

With the acceleration of information technology in university management and the demand of university information continues to strengthen, the campus card is widely used in all aspects of student life, and requires data mining analysis for each sector basis for decision making. As one of the most popular data mining technologies, clustering algorithms are widely used for campus card consumption data mining. Howerve, people don’t know which clustering algorithm is the most suitable for campus card data. This paper carries out experiments with multiple widely used clustering algorithms, obtaining the most appropriate data mining clustering algorithm for the data, and analyzes the reason.