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

doi:10.13474/j.cnki.11-2246.2017.0224

Abstract

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

CLC Number:

P208

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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