计算机工程与应用2019，Vol.55Issue(10)：83-89,7.DOI:10.3778/j.issn.1002-8331.1801-0471
时空属性关系标签的频繁轨迹模式挖掘
Frequent Trajectory of Pattern Mining with Spatio-Temporal Attribute and Relationship Label
摘要
Abstract
The wide application of campus card technology is an important symbol to measure the informatization degree in colleges. Among them, the students’consumption data imply a great potential value, and it is a significance to exca-vate. Motivated by this problem, this paper proposes a method to convert campus streaming data into a consumption tra-jectory tree DP-DBSCAN algorithm with spatial attribute, and builds FP-TRtree mining model with relationship label. DP-DBSCAN algorithm adopts the time block, order query and distance measurement, it can transfer data into FP-TRtree frequent item set with order effectively, ignoring parameters. The FP-TRtree model adds value in sequence, supports descending order, and optimizes the relationship label between the same loci. Visualization analysis demonstrates that the method and model not only find the student relationship track network of frequent consumption and isolated populations, but also make a quantitative description between nodes of students spending intimate degree. At the same time, the method reduces the database scan times and the establishment of the branch of a tree. The experimental results conform to real consumption of students, it can find hidden information from the complex consumption network and provide the basis for school management.关键词
DP-DBSCAN算法/一卡通数据/关系标签/FP-TRtree模式/可视化Key words
DP-DBSCAN algorithm/cartoon data/relationship label/FP-TRtree mode/visualization分类
信息技术与安全科学引用本文复制引用
潘晓英,赵倩,赵普..时空属性关系标签的频繁轨迹模式挖掘[J].计算机工程与应用,2019,55(10):83-89,7.基金项目
国家自然科学基金重点项目(No.51437003) （No.51437003）
吉林省科技发展计划重点项目(No.20180201092GX) （No.20180201092GX）
吉林省科技发展计划项目(No.20160623004TC). （No.20160623004TC）
