基于K均值聚类的定位算法分析

Localization algorithm analysis based on K-means clustering

在描述了聚类算法的基本思想和概念的基础上,介绍了一种常见的聚类算法—K均值和K中心点聚类算法,通过处理认知无线电网络中主用户定位在海量数据中应用K均值聚类算法,对该算法进行分析,仿真结果表明:与传统的主用户定位算法相比,使用K均值聚类算法能够有效地提高定位精度和降低定位算法的复杂度.

This paper introduces the basic ideas and concepts of the clustering algorithm and presents a common clustering algorithm K-means and K-means center clustering algorithm. Then analysis of the primary users localization in cognitive radio is conducted by using K-means clustering algorithm. The results show that the proposed K-means localization algorithm performs lower computational complexity and smaller location error than traditional schemes.