传统的聚类算法在考虑类与类之间的连通性特征和近似性特征上往往顾此失彼.首先给出类边界点和类轮廓的基本定义以及寻求方法,然后基于类间连通性特征和近似性特征的综合考虑,拟定一些类间相似性度量标准和方法,最后提出一种基于类轮廓的层次聚类算法.该算法能够有效处理任意形状的簇,且能够区分孤立点和噪声数据.通过对图像数据集和Iris标准数据集的聚类分析,验证了该算法的可行性和有效性.%Traditional clustering algorithms are often incapable of roundly considering the connectivity and similarity characteristics among classes. The thesis firstly presents the fundamental definition of class boundary point and class profile; secondly, with comprehensive consideration based on connectivity characteristics and similarity characteristics among classes, defines some standards and methods for inter class similarity measurement; thirdly, proposes a class-profile-based hierarchical clustering algorithm, which is able to effectively process arbitrary shaped clusters and distinguish isolated points from noise data. The feasibility and effectiveness of the algorithm is validated through clustering analysis on image data sets and Iris standard data sets.
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