几种不同的方法在GPS 大数据 探查中的应用分析

GPS 定位系统对车辆的运行调控以及拥堵性分析具有重要意义。但定时采样的GPS 数据难免存在坏点的情 况，而坏点的存在对分析结果容易产生较大错误，从而影响交通管理决策。本文通过高斯混合模型、K-均值聚类分析以及 SOM 自组织神经网络三种方法完成对原始数据时间段划分、字段提取以及坏值清理的操作。这三种方法主要用于对数据进 行聚类分析，根据分析结果识别孤立点从而进行清理。结果显示，高斯聚类与K-均值聚类算法的坏点识别精度小于SOM 自 组织神经网络，但前两种算法的运行效率较后者高。

Analysis of the Application of Several Different Methods in GPS Big Data Exploration

It is significant for the GPS positioning system to control the vehicle and analyzes the congestion. But there are bad values in GPS sampling data, easy to produce large error on the analysis results, thus affecting the traffic management decision. This paper completes the original data segment, field extraction and bad values cleaning using Gauss mixture model, K- means clustering analysis and SOM self-organizing neural network separately. Thees three methods are mainly used for data clustering analysis, cleaning the isolated points according to the results. The results show that the recognition accuracy of Gauss clustering and K- clustering algorithm is less than SOM self-organizing neural network, but the operating efficiency of the first two algorithms is better than the latter.