实现的cure聚类的demo。算法在开始时，每个点都是一个簇，然后将距离最近的簇结合，一直到簇的个数为要求的K。它是一种分裂的层次聚类。算法分为以下6步：

1）从源数据对象中抽取一个随机样本S。

2）将样本S分割为一组划分。

3）对划分局部的聚类。

4）通过随机取样提出孤立点。如果一个簇增长得太慢，就去掉它。

5）对局部的簇进行聚类。

6）用相应的簇标签标记数据。(The implementation of the cure clustering of the demo. At the beginning of the algorithm, each point is a cluster, and then the nearest cluster is combined to the number of clusters K. It is a hierarchical cluster of divisions. The algorithm is divided into the following 6 steps:

1) extract a random sample S from the source data object.

2) the sample S is divided into a set of partitions.

3) to divide the local clustering.

4) the isolated point is proposed by random sampling. If a cluster is growing too slowly, get rid of it.

5) clustering the local clusters.

6) mark the data with the corresponding cluster tag.)