搜索资源列表
K均值
- 本程序通过k均值算法对两类进行分类。通过任意选择初始点，由k均值很快找到两类的中心点-the procedure k means algorithm to classify two types. Through arbitrary choice initial point, k Mean quickly found two focal point
K均值算法
- 实现K均值算法，读取文件，实现K均值的分类。-K-means algorithm to achieve, reading the paper, K-mean achievement category.
K-meanCluster
- How the K-mean Cluster work Step 1. Begin with a decision the value of k = number of clusters Step 2. Put any initial partition that classifies the data into k clusters. You may assign the training samples randomly, or systematically as the f
k-mean
- k-均值算法 做的图象分割实例 （将图片像素聚成三类）
k-mean
- 算法描述： K均值算法： 给定类的个数K，将N个对象分到K个类中去， 使得类内对象之间的相似性最大，而类之间的相似性最小。
k-mean
- K-means聚类算法的java实现描述！有详尽的说明，对初学者非常有用！
JAVA实现文本聚类，用到TF/IDF权重
- JAVA实现文本聚类，用到TF/IDF权重，用余弦夹角计算文本相似度，用k-means进行数据聚类等数学和统计 知识。,JAVA realization of text clustering, using TF/IDF weight, calculated using cosine angle between the text of similarity, using k-means clustering for data such as mathematical and statistical
KMeansJava
- 利用Java实现的K-均值算法，K-Mean 分群法是一种分割式分群方法，其主要目标是要在大量高纬的资料点中找出 具有代表性的资料点；这些资料点可以称为群中心，代表点；然后再根据这些群中心，进行后续的处理，可用于数据挖掘中的聚类分析-Java implementation using K-means algorithm, K-Mean grouping method is a fragmented grouping method, whose main goal is to a large nu
knn
- k最邻近算法，经典的分类算法，绝对有帮助-k-nearest neighbour algorithm,it is a classical algorithm for text cluster
Fast-K-means-clustering
- Fast mex K-means clustering algorithm with possibility of K-mean++ initialization (mex-interface modified from the original yael package https://gforge.inria.fr/projects/yael) - Accept single/double precision input - Support of BLAS/OpenMP
kmeans-image-segmentation
- K-meansK均值聚类在无监督的情况下选择图像特征的算法-K-meansK means clustering in the case of unsupervised image feature selection algorithm
rbf_Kmeans
- Matlab环境下实现的RBF神经网络K均值聚类算法-Matlab environment to achieve the RBF neural network K-means clustering algorithm
K均值聚类在基于OpenCV的图像分割中的应用
- 介绍了传统的图像分割与K-均值聚类算法分割，然后利用OpenCV函数将其实现，并介绍了OpenCV中图像分割相关的基本函数。(This paper introduces the segmentation of traditional image segmentation and K- mean clustering algorithm, then uses OpenCV function to implement it, and introduces the basic functions of
k-means
- k均值，数据已经有了，主要用于分类，美列都是一类数据，只用了其中一部分，数据是自己编的。(K mean, data already exists, mainly for classification, the United States column is a kind of data, only a part of the data is their own series.)
K-means
- 用matlab实现k均值算法，不用库函数(Implementation of K mean algorithm)
k均值聚类
- 用VC++写的K均值聚类算法，可以直接使用(K mean clustering algorithm is written by VC++ , which can be used directly.)
K-mean Clustering and RBF _V_1.0
- Radial Basis Function with K Mean Clustering using Pseudo inverse method
K-means
- 利用MATLAB实现K均值聚类算法，加深对该算法的理解。(We use MATLAB to achieve K mean clustering algorithm to deepen our understanding of the algorithm.)
k均值聚类算法
- 根据k均值聚类的原理，实现一些数字的聚类，但是具体类别数需要自己设置(Clustering of some numbers by K mean clustering)
K-means
- 对图像用k-means算法进行处理，得到效果更好的图像(Processing the image with k-means algorithm)