搜索资源列表
K-Means
- 数据挖掘k均值聚类 vc++ 环境运行-data minning cluster k-method
accord-kmeans-source
- K-Mean clustering source code
K-Means_Text_Cluster
- K-Means文本聚类python实现，文本聚类算法，人名排除歧义-Text Cluster by the algorithm of K-means(include texts)， discrimination of name ambiguity.
PK-means
- K-means聚类算法，用于文件、数据的聚类分析-K-means clustering algorithm for document clustering analysis of the data
K
- 这是K均值聚类的程序，数据挖掘等课程都需要-This is a K-means clustering procedures, data mining courses
K-MEANS1
- K-MEANS for clistering by in C-K-MEANS for clistering by in C++
gak-means
- 遗传算法 优化K均值算法 实现两种算法的优势互补 -Genetic Algorithm K-means algorithm to achieve complementary advantages of the two algorithms
k
- k均值算法，数据挖掘里面比较基础的算法，实现类聚-k-means algorithm, which based on the comparison of data mining algorithms to achieve clustering
k-mean
- K均值（用matlab实现花的分类，附注释，程序简单）-K-means (machine learning job classification flowers)
k
- k means clustering matlab code
K
- k-means 聚类 有归一化和选择中心点函数-k-means cluster
k-mean
- 简单的k_mean算法 对k均值算法学习很有帮助，也可以在此基础上学习改进算法-Simple algorithm for k-means algorithm k_mean very helpful, you can learn on the basis of improved algorithm
K-means_C
- K均值聚类算法，由C语言编写，可以自己调整迭代次数，超级好用-K-means clustering algorithm, the C language, you can adjust their iterations, super easy to use
means
- 本附件中是关于K-means阈值分割的代码，主要用于图像分割领域-it is k-means image processing code
csk-means
- 这是一种我自己比较满意的K-means聚类算法，通过对原始的K-means进行改进而来。-This is a way I quite satisfied with K-means clustering algorithm, through the original K-means to improve from.
k-mean-clustering
- k-means algorithm descr iption with examples with visual basic code.
RBF_K-means
- 考虑Hermit多项式的逼近问题 ，用k-means训练RBF网络-Consider Hermit polynomial approximation problem with k-means training RBF network
Classification K-NN
- classification des image satilitaire par k-means
K聚类
- 聚类，这是一个用MATLAB编写的kmeans算法，主要用通常的聚类分析(Clustering, which is a MATLAB written in kmeans algorithm, mainly with the usual clustering analysis)
k-means_old-faithful-master
- k-means属于划分法的聚类算法，能够见数值样本按照一定的规律划分为用户指定的类别(The goal of the algorithm is to partition a given dataset into a user-specified number of clusters, k, and obtain the similarity between samples of the same cluster rather than the different clusters)