搜索资源列表
K-means_clustering_demo
- K-均值聚类算法 vc++图形演示程序-K-means clustering algorithm c++ demo program
K-means
- K-means K聚类算法的C++语言实现，里面附有使用文档和实例，在VS2005上实现的-K-means K clustering algorithm C++ language, which accompanied with documentation and examples implemented on the VS2005
K_means
- K-means MATLAB源程序，适用于聚类分析的代码-K-means MATLAB programe
K-Means
- 用于图像处理，K-MEANS聚类实现图像的分割和识别，是一种重要的图像处理方法-For image processing, K-MEANS Clustering achieve image segmentation and recognition, is an important image processing method
k-means
- k均值聚类算法源码 聚类算法学习的实例功能-k-means cluster algorithm
k
- k平均聚类所谓k均值聚类方法是一种无监督的学习算法，它能用已知类数的数据聚类和预测。-k-average clustter
K-means
- K-means聚类算法实现代码，在VC++环境下开发的，令附有实验样本集。-K-means clustering algorithm code, in the VC++ development environment, so that experiments with sample set.
k-means
- k-means算法实现WEB搜索，用模拟退火，对K-means聚类算法进行数据挖掘-k-means algorithm WEB search
K-MEANS
- 数据挖掘，K-means源码，数据集为iris-Data mining, K-means source code for the iris data set
k-means
- k-均值聚类算法c语言版，经验证测试是可以运行
K-means
- kmeans算法代码，已经通过验证，可以正常使用，没有病毒
k-means
- k-means算法 ，应 用 于 数 据 挖 掘 中。-k-means algorithm is applied to data mining
K-means
- k均值聚类算法,初始随机给定k个簇中心，根据邻近原则，把待分类的样本点分到各个簇。-k-means clustering algorithm,which is applied in RBF neural network.
k-means-algorithm-
- 在matlab开发环境下用k均值算法实现数据的分类，以及得到数据的聚类中心- realizingthe data classification With k-means algorithm
K-Means
- K均值，二维算法程序，visual c++ 环境下运行-K-means Algorithm routine in two dimension。It can run in visual c++ environment
K-means
- 使用k-means算法对图像进行分割，并利用遗传算法对k-means算法加以改进(The k-means algorithm is used for the segment of images, and the genetic algorithm is used to improve the k-means algorithm)
K-means
- 在里面的的是一些关于k-means的东西，用的mnist数据(I try the Mnits data and use K-means to doing the clustering)
k-means-master
- k-means algorithm with matlab test code is very perfect
K---MEANS
- 随机生成1000个二维坐标点并用K-means算法计算聚类结果(1000 two-dimensional coordinate points are generated randomly and the clustering results are calculated by K-means algorithm)
k-means程序
- 介绍了k-means 均值聚类，能很好的将离散的点，聚类成几个指定的聚合点。(The K-means mean clustering is introduced, and the discrete points can be well clustered into several designated aggregation points.)