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。
This clustering algorithm can on-line partition the input data, pointwise update the clusters, and self-organize the fuzzy neural structure.
此聚类算法可以在线地划分输入数据，逐点地更新聚类，自己组织模糊神经网络的结构。
The classical C-means clustering algorithm (CMA) is a well-known clustering method to partition an image into homogeneous regions.
经典的C -均值聚类算法（CMA）是将图像分割成C类的常用方法，但依赖于初始聚类中心的选择。
So an energy and distance efficient clustering algorithm based on virtual area partition was proposed in this paper for heterogeneous wireless sensor networks.
提出了一种基于虚拟区域划分的适用于异构无线传感器网络的能量和距离有效分簇算法。
Experiments show that the algorithm has good clustering qualities when it is used to partition customers.
实验表明，该算法用于对银行客户细分有较好的聚类效果。
The performance shows that D-CURE algorithm can effectively resolve the clustering partition problem in distributed environment.
实验证明，D-CURE算法可以很好地解决分布式环境下聚类分区问题。
Second, after all clustering algorithms are described in brief, the partition algorithm which is related to this paper is proposed, and the method of selecting primary centers is also proposed.
其次，在对各种聚类算法进行简单描述后，提出了本文所涉及到的基于划分的聚类算法，并提出了本文中的算法对聚类分析中普遍存在的初始中心选择问题的处理方式。
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。
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。
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