hierarchical clustering method meaning in Chinese
系统聚类法
Examples
- Hierarchical clustering method
系统聚类法 - In the field of clustering , by comparing some clustering methods and analysing characteristic of science data , we propose an improved hierarchical clustering method synthetic idea of k - means method
在聚类方面,经过比较各种聚类算法和分析科学数据的特点,提出了结合k -平均思想的改进型系统聚类算法。 - Then the methods of ontology integration is studied , which falls into two main steps : using hierarchical cluster method to find similar concepts and using heuristic rule to merging similar discovered concepts
本文接着对本体集成方法进行详细研究。本体集成过程为先利用聚类算法来找出相似概念,再利用启发式规则进行相似概念合并处理。 - It learns from the basic thought of hierarchical clustering methods ( hcm ) , which groups objects by comparing the sizes of distance or similar coefficients between objects , meanwhile , combines with the optimal split - plot designs ( ospd ) in ordered samples , and synthesizes the intuitive property of hcm and the character of simplicity and the ability in finding out the accurate solution of ospd . with history data , this paper assumes that data from the same group come from the same distribution , and so do the history data
本文汲取了系统聚类法中通过定义距离或相似系数并以其大小将对象进行分类的基本思想，将之与有序样本情况下的最优分割法相结合，吸收了系统聚类法的直观性和最优分割法的简捷性及可以求出精确最优解的良好性质，在存在历史数据的条件下，假设同类数据来自于同一分布，历史数据相应的来自该分布。 - The basic idea for hierarchy - based method is that creating and maintaining a tree of clusters and sub - clusters according to some kind of criterion to measure the distance of clusters , the procedure will be sloped until some terminal conditions are satisfied . hierarchical clustering method can be further classified into agglomerative and divisive hierarchical clustering , depending on whether the hierarchical decomposition is formed in a bottom - up or top - down fashion . most hierarchical clustering methods can produce the better results when the clusters are compact or spherical in shape . but they do not perform well if the clusters are any shape or there are outliers . a main reason is that the most hierarchical clustering methods employ medoid - based measurement as distance between clusters
基于层次方法的聚类的基本思想足：根据给定的簇间距离度量准则，构造利维护一棵由簇利子簇形成的聚类树，直至满足某个终结条件为止。根据层次分解是自底向上还是自顶向下形成，层次聚类方法可以分为凝聚的( agglomerative )和分裂的( divisive ) 。人多数层次聚类算法在紧密簇或球形簇结构下能够产生较好的聚类效果。