基于密度与划分方法的聚类算法设计与实现

在分析常用聚类算法的特点和适应性基础上提出一种基于密度与划分方法的聚类算法。该算法根据数据对象密度分布状态来自动确定聚类簇密度吸引中心点和聚类簇的初始划分;然后利用划分的方法,根据密度可达定义来寻找密度可达数据对象簇,从而完成数据对象簇的最终聚类。实验证明该算法能够很好地处理具有任意形状和大小的簇,能够有效地屏蔽噪声和离群点的影响和发现孤立点;同时也减小了输入参数对领域知识的依赖性。

Design and implementation of clustering algorithm based on density and partition method

A clustering algorithm based on density and partitioning method is presented according to the analysis of the strengths and weaknesses of traditional clustering algorithms.The algorithm can automatically locate the dense centers of clusters,and deter- mine initial partitions of the clusters.On the basis of initial partitions of the clusters,density reachable clusters of data objects are found out by using partitioning method,and the final clusters are produced.The experimental results demonstrate that the algo- rithm can handle clusters of arbitrary shapes and sizes,minimize the influences of noise and deviation of data objects,and locate the outliers.At the same time,the algorithm can minimize the dependency of input numbers on specialist knowledge.