摘要： 针对移动用户出行模式识别过于复杂的问题,提出一种基于密度与动态阈值的任意形状聚类挖掘算法。利用数据预处理技术提取移动用户在各路段的速度,通过MCFT树构造用户速度分类模型,采用DBSCAN方法进行核心子聚类合并,最终生成MCFT树任意形状的聚类结果,从而达到利用动态速度阈值有效识别用户出行模式的目的。实验结果表明,通过DVTD算法得到动态速度阈值识别用户出行模式具有较高的可行性,提高了用户出行识别的准确性。

关键词: DVTD算法, 聚类特征树, 簇, 用户出行模式, 动态速度阈值

Abstract: Aiming at the problem that the mobile user travel pattern recognition is too complicated,an arbitrary shape clustering algorithm based on density and dynamic threshold is proposed.The data preprocessing technology is used to extract the speed of mobile users.User speed classification model is constructed through the MCFT tree.DBSCAN method is used to merge the core subclusters.The clustering results of the arbitrary shape of the MCFT tree are generated,so as to achieve the effective use of dynamic speed threshold to identify the user travel pattern.Experimental results show that it is feasible to identify the user’s travel pattern by using the DVTD method to get the dynamic speed threshold,which can effectively improve the accuracy of user travel identification.

Key words: DVTD algorithm, Clustering Feature(CF) tree, cluster, user travel pattern, dynamic speed threshold

中图分类号: 

• TP391.4