Description: 数据挖掘算法之一，基于代表点的CURE聚类算法，该算法先把每个数据点看成一类，然后合并距离最近的类，直至类个数为所要求的个数为止。-CURE cluster algorithm based on representive point,one of data mining algorithms,classifies each data as a category firstly, then unifies categories with the nearest distance into one until the number of class is coincidence with the classes demanded. Platform: |
Size: 45056 |
Author:黄镇 |
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Description: CURE（Clustering Using Representatives）是一种针对大型数据库的高效的聚类算法。基于划分的传统的聚类算法得到的是球状的，相等大小的聚类，对异常数据比较脆弱。CURE采用了用多个点代表一个簇的方法，可以较好的处理以上问题。并且在处理大数据量的时候采用了随机取样，分区的方法，来提高其效率，使得其可以高效的处理大量数据。-CURE (Clustering Using Representatives) is a database for large and efficient clustering algorithm. Based on the breakdown of the traditional clustering algorithm is spherical, equal-sized cluster of abnormal data more vulnerable. CURE adopted by a number of points on behalf of a cluster approach can better deal with these questions. And deal with large amount of data when using the random sampling, area way to improve its efficiency so that it can handle large amounts of data and efficient. Platform: |
Size: 21504 |
Author:肖宪 |
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Description: 聚类算法实验,采用两种不同类型的聚类算法:基于划分的聚类方法k-means和基于层次的聚类方法CURE，采用的数据集是：Iris Data Set，数据集中共包含150组数据信息。
材料中有详细的说明文档，具体介绍了算法实现的细节，很容易理解-Clustering algorithm experiment, using two different types of clustering algorithm: Partition-based clustering method k-means and hierarchical clustering method based on CURE, the use of the data set is: Iris Data Set, the data set contains a total of 150 sets of data information .
Materials are described in detail in the documentation, the specific details described algorithm, it is easy to understand Platform: |
Size: 189440 |
Author:牛魔王 |
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Description: java语言编写的Cure聚类算法，实现对数据点的聚类-java language Cure clustering algorithm to achieve the clustering of data points Platform: |
Size: 15360 |
Author:崔序 |
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Description: python语言编写的一个Cure算法，可实现数据的分类-A cure algorithm programmed in python which an realize data clustering Platform: |
Size: 10240 |
Author:Lee |
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Description: 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.) Platform: |
Size: 161792 |
Author:Aileen00080 |
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