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1·Method Weighted Hierarchical Clustering method was used.

方法基于加权的层次聚类方法。

2·Popular approaches include k-Means and hierarchical clustering.

流行的方法包括k - Means和分层集群。

3·Partition clustering and hierarchical clustering are two fundamental clustering methods.

划分聚类和分级聚类是两种基本的聚类手段。

4·The experimental data were used for similarity calculation and hierarchical clustering analysis.

用于相似度计算和聚类分析实验数据。

5·Objective The conditional hierarchical clustering for 1-dimensional (1-d) ordinal data was discussed.

目的介绍并讨论一种适用于一维有序样品的条件系统聚类方法。

6·Common approaches to unsupervised learning include k-Means, hierarchical clustering, and self-organizing maps.

无监管学习的常见方法包括k - Means、分层集群和自组织地图。

7·It consists of three parts: calculation of distance measures, randomized testing, and hierarchical clustering.

该算法由三部分组成：距离测度计算、随机化检验和系统聚类。

8·In this paper, cluster ensemble methods based on hierarchical clustering and clustering validity have been studied.

本文对基于层次聚类的簇集成方法及聚类的有效性进行了研究。

9·The cluster distance computing method is the key issue affecting the performance of hierarchical clustering algorithm.

在聚类的过程中簇间距离计算的准确性是影响算法性能的重要因素。

10·The data are reduced in both horizontal and vertical directions by using hierarchical clustering and rough set methods.

通过系统聚类和粗糙集两种方法进行数据约简，使数据得到横向和纵向两个方向上的约简。