分布式增量机制下的交通流大数据聚类分析

doi:10.13474/j.cnki.11-2246.2017.0224

测绘通报 ›› 2017, Vol. 0 ›› Issue (7): 61-65.doi: 10.13474/j.cnki.11-2246.2017.0224

分布式增量机制下的交通流大数据聚类分析

李欣^1,2

1. 河南财经政法大学中原经济区“三化”协调发展河南省协同创新中心, 河南郑州 450046;
2. 河南财经政法大学资源与环境学院, 河南郑州 450046

收稿日期:2016-10-12 修回日期:2017-01-24 出版日期:2017-07-25 发布日期:2017-08-07
作者简介:李欣(1981-),男,博士,讲师,主要研究方向为地理信息系统理论研究与实践应用。E-mail:lixin992319@163.com
基金资助:
国家自然科学基金（41501178）；河南财经政法大学博士科研启动基金（800257）

Traffic Flow Big Data Clustering Analysis Method Based on Distributed Incremental Mechanism

LI Xin^1,2

1. Collaborative Innovation Center of Three-aspect Coordination of Central Plain Economic Region, Henan University of Economics and Law, Zhengzhou 450046, China;
2. College of Resource and Environment, Henan University of Economics and Law, Zhengzhou 450046, China

Received:2016-10-12 Revised:2017-01-24 Online:2017-07-25 Published:2017-08-07

摘要/Abstract

摘要： 时空聚类分析是对时空大数据进行利用的一种有效手段。本文提出了一种分布式增量大数据聚类分析方法，利用分布增量机制不但可以减少重复计算和迁移拷贝次数，而且可以持续对聚类结果进行修正，能够在保持聚类准确性的条件下提升整体运算效率。而聚类算法本身通过数据聚集趋势预分析、聚类算法和结果评价3个步骤，构建了一体化时空邻域，在时间和空间维度保证了聚类结果的准确性。经过试验证明该方法可以实现时空大数据的快速高效信息挖掘。

关键词: 时空数据, 大数据, 聚类分析, 增量聚类, 时空邻域

Abstract: Spatio-temporal clustering analysis is an effective way of using spatio-temporal big data. This paper proposes a distributed incremental big data clustering analysis method. The incremental distribution mechanism can not only reduce the repeated calculation and the number of copies, but also can modify the clustering results continuously. And it is able to improve the operational efficiency under the condition of keeping in clustering accuracy. The clustering algorithm includes three steps:data aggregation trend analysis, clustering algorithm and result evaluation. It constructs an integrated spatio-temporal neighborhood, which guarantees the accuracy of clustering results in time and space. The experiments show that this method can realize the fast and efficient information mining of spatio-temporal big-data.

Key words: spatio-temporal data, big data, cluster analysis, incremental clustering, spatio-temporal neighborhood

中图分类号:

P208

李欣. 分布式增量机制下的交通流大数据聚类分析[J]. 测绘通报, 2017, 0(7): 61-65.

LI Xin. Traffic Flow Big Data Clustering Analysis Method Based on Distributed Incremental Mechanism[J]. 测绘通报, 2017, 0(7): 61-65.

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分布式增量机制下的交通流大数据聚类分析

Traffic Flow Big Data Clustering Analysis Method Based on Distributed Incremental Mechanism

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