Based on Chameleon algorithm,DPC algorithm and mudule density function,a hierarchical clustering algorithm based on density is proposed.In the first stage of Chameleon algorithm,DPC algorithm is used for data processing.In the second stage,when clusters are merged with the approximation function,module density that characterizes the similarity of data within a cluster is introduced.When module density achieves maximum,termination condition and final clustering result are obtained.An improved Chameleon algorithm is used to establish dynamic model,which can automatically determine the termination condition,and can identify many shape clusters,helping traditional Chameleon algorithm find the clustering end and be less sensitive to the initial parameter setting.%本研究基于Chameleon算法结合DPC算法和模块密度函数,提出一种基于密度的层次聚类算法.在Chameleon算法第一阶段,引入DPC算法进行数据处理,在第二阶段,利用近似度函数进行子簇合并时,引入表征簇内数据点相似程度的函数——模块密度,当模块密度值最大时获得最终聚类结果.本算法利用上述方法建立动态模型,可自动确定终止条件,并且可以识别任意形状簇,克服了传统Chameleon算法无法找到聚类终点和对于初始参数设置敏感的问题.
展开▼
