Partition clustering algorithms 演算法
height partition clustering 高度划分聚类
spatio-temporal partition clustering 时空分区聚簇
Partition-based clustering 基于划分的方法
clustering partition 聚类分区
As the accuracy of partition clustering closely related to the choice of start points,LE can be used to decide whether the start points should be reset and begin another clustering process,in order to get a better partition.
由于划分聚类的结果多与初始点相关,关联熵实际上可以辅助决定是否需要再选择初始点,重新聚类,以获得一个较好的聚类结果。
参考来源 - 关联熵及其应用·2,447,543篇论文数据，部分数据来源于NoteExpress
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。
Based on clone selection theory and typical partition clustering approach, a new clustering algorithm is proposed.
将克隆选择原理同典型的划分聚类方法结合起来，提出一种克隆选择聚类算法。
As a classical partition clustering algorithm, CLARANS USES local search with random restart to find clusters central points.
CLARANS算法是经典的划分聚类算法，其核心思想是采用随机重启的局部搜索方式搜索中心点。
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