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Description: 层次聚类算法中的cure算法，它利用代表点来达到聚类目的-hierarchical clustering algorithm cure the algorithm, which uses representatives point to achieve clustering purpose
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Size: 43201 |
Author: 林恩 |
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Description: 经典cure聚类算法,实现用c++语言.大家-classic cure clustering algorithm, and using the c language. We look at
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Size: 44284 |
Author: 孔明 |
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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。
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Size: 21916 |
Author: 肖宪 |
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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.
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Size: 45056 |
Author: 黄镇 |
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Description: 层次聚类算法中的cure算法，它利用代表点来达到聚类目的-hierarchical clustering algorithm cure the algorithm, which uses representatives point to achieve clustering purpose
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Size: 43008 |
Author: 林恩 |
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Description: CURE算法实现 一种分层次的聚类算法 效果较好-CURE algorithm to achieve a hierarchical clustering algorithm better results
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Size: 43008 |
Author: 龙卑鄙 |
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Description: 经典cure聚类算法,实现用c++语言.大家-classic cure clustering algorithm, and using the c language. We look at
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Size: 44032 |
Author: 孔明 |
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Description: 层次聚类算法中的Cure算法，可以用于识别非球形的簇，解决了偏好球形和相似大小的问题，在处理孤立点上也更加健壮。-Hierarchical clustering algorithm of Cure algorithm can be used to identify non-spherical cluster, the preferred solution is similar to spherical and the size of the problem, in dealing with isolated points and more robust.
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Size: 6144 |
Author: 刘嘉良 |
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Description: 一 种改进的 CURE聚类算法-An improved clustering algorithm CURE
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Size: 206848 |
Author: hehe |
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Description: 介绍一种非常有效的对大数据库的数据挖掘算法-数据聚类-A very effective introduction to the large database of data mining algorithms- Data Clustering
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Size: 1482752 |
Author: huwenan |
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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.
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Size: 21504 |
Author: 肖宪 |
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Description: 用c++实现的CURE聚类算法 这是即DBSCAN算法后出现的基于密度的聚类算法-With c++ Realized CURE clustering algorithm DBSCAN algorithm that is, this is occurring after the density-based clustering algorithm
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Size: 44032 |
Author: 刘年 |
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Description: 用matlab实现聚类算法，注意是层次聚类和未知类别聚类算法！-Clustering algorithm using matlab implementation, pay attention to are hierarchical clustering and unknown type of clustering algorithm!
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Size: 16384 |
Author: 张晓飞 |
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Description: Implementation of CURE Clustering Algorithm
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Size: 194560 |
Author: Akil ELKAMEL |
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Description: CURE: AN EFFICIENT CLUSTERING ALGORITHM FOR LARGE DATABASES-CURE: AN EFFICIENT CLUSTERING ALGORITHM FOR LARGE DATABASES
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Size: 1134592 |
Author: Maha |
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Description: cure clustering code
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Size: 11688960 |
Author: rocket |
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Description: cure聚类算法源程序 matlab 十分好用 聚类效果不错(Cure clustering algorithm source code, Matlab is very easy to use, clustering effect is good)
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Size: 1169408 |
Author: luozhiming
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Description: CURE算法是聚类算法的一种，源代码，可直接运行(CURE algorithm is a clustering algorithm, the source code can be run directly)
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Size: 41984 |
Author: Lena
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Description: Research paper on cure clustering
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Size: 1224704 |
Author: musaje |
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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.)
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Size: 161792 |
Author: Aileen00080 |
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