基于传感器网络的K-均值聚类算法研究

现有的传感器网络数据处理系统只能向用户提供简单的查询操作,如何高效地处理传感器网络的海量数据流,从中获取有用的知识成为新的挑战.提出了一种基于传感器网络的分布式K一均值聚类算法,首先由中心点下发k个质心的初始值,各个节点将数据对象赋于质心距离最近的簇,并将簇的信息通过传感器网络逐层上传合并;然后中心点计算k个簇中对象的平均值,再下发,反复迭代,直到各个簇满足误差准则,得到最后的聚类结果.实验证明,该算法准确率较高,计算时间较短.

Research on k-means clustering arithmetic based on sensor networks

Existing data processing system of sensor networks provide with simply query only,how to process the large data streams in sensor networks efficiently and how to find interesting knowledge in these streams become new challenge.A distributed k-means clus-tering algorithm based on sensor networks is presented.At first,the central node generates k centroids and broadcast in the network.Thereafter,each sensor node assigns each point in their local dataset to the nearest centroid,and transmits their local k clusters information to their parents node to merge.For each cluster,the central node recomputes the centroid as the average of data points assigned to it.If it doesn't meet the stop condition,the algorithm will iterates the process from the start.In the end,the clustering result is gotten.Tests prove this arithmetic high veracity and fewer computing time.