分布协作式对等网络中大规模空间数据挖掘方法研究

分布协作式对等网络较为复杂,而空间数据规模大,当前数据挖掘方法很难实现对其的准确挖掘。为此,提出一种新的分布协作式对等网络中大规模空间数据挖掘方法,给出分布协作式对等网络的GIS应用架构,在此基础上对分布协作式对等网络进行无向环路遍历,获取分布协作式网络的全部环路,挖掘出目的空间数据所属社区。通过痕迹系数判断目的空间数据流是否经过该社区,如果目标空间数据流经过该社区,则通过计算相关系数获取某个时刻目标空间数据流在社区中的位置,从而实现大规模空间数据挖掘。实验结果表明,采用所提方法对分布协作式对等网络中大规模空间数据进行挖掘,有很高的挖掘有效性,而且挖掘效率和挖掘精度均较高。

Distributed collaborative peer-to-peer network massive spatial data mining method in the study

the distributed collaborative peer-to-peer network is relatively complex, and large scale spatial data, the data mining method is difficult to realize the accurate mining.For this, put forward a new kind of distributed collaborative peer-to-peer network massive spatial data mining methods, gives a distributed collaborative peer-to-peer network GIS application architecture, on the basis of the distributed collaborative peer-to-peer networks without the loop traverse, all access to distributed collaborative network loop, purpose of excavated space data belongs to the community, through the coefficient of trace whether objective space data flow through the community, if the target space data flow through the community, by computing the correlation coefficient for some point target space is the position of the data flow in the community, so as to realize the large-scale space data mining.The experimental results show that the proposed method for distributed collaborative peer-to-peer network massive spatial data mining, a high degree of effectiveness, efficiency and accuracy of mining and mining are higher.