搜索资源列表
UPublic
- 一个用delphi 写的图像,K均值(K-means)聚类算法-Written with the image of a delphi, K mean (K-means) clustering algorithm
kmean
- k-means 算法的工作过程说明如下：首先从n个数据对象任意选择 k 个对象作为初始聚类中心；而对于所剩下其它对象，则根据它们与这些聚类中心的相似度（距离），分别将它们分配给与其最相似的（聚类中心所代表的）聚类；然后再计算每个所获新聚类的聚类中心（该聚类中所有对象的均值）；不断重复这一过程直到标准测度函数开始收敛为止。-k-means algorithm process as follows: First of all, the object data from the n choose k
engdemo
- the classic pattern recognition algorithms, dynamic clustering algorithm k mean using Matlab programming, as well as classification of the class analysis
K_MeansAlgo
- 改进的K-Means算法，通过改进传统K-Means算法，剔除远离中心均值的离散点，加快算法的收敛速度。-Improved K-Means algorithm, by improving the traditional K-Means algorithm, removing the mean of discrete points away from the center to accelerate the convergence speed.
k_means_cluster
- k均值聚类算法 ，c语言实现 了基于均值的聚类分析，同时增加了多维向量分析功能，使得聚类的收敛速度更快。-k means clustering algorithm, c language implemented based on the mean cluster analysis, while increasing the multi-dimensional vector analysis functions, making the convergence faster clustering.
NewK-means-clustering-algorithm
- 珍藏版，可实现，新K均值聚类算法，分为如下几个步骤： 一、初始化聚类中心 1、根据具体问题，凭经验从样本集中选出C个比较合适的样本作为初始聚类中心。 2、用前C个样本作为初始聚类中心。 3、将全部样本随机地分成C类，计算每类的样本均值，将样本均值作为初始聚类中心。 二、初始聚类 1、按就近原则将样本归入各聚类中心所代表的类中。 2、取一样本，将其归入与其最近的聚类中心的那一类中，重新计算样本均值，更新聚类中心。然后取下一样本，
Kmeans-julei
- 动态聚类的k均值算法,采用matlab编程,内有多个子程序，和一个主程序-dynamic clustering algorithm k mean using matlab
kmeans
- k-means 算法接受参数 k ；然后将事先输入的n个数据对象划分为 k个聚类以便使得所获得的聚类满足：同一聚类中的对象相似度较高；而不同聚类中的对象相似度较小。聚类相似度是利用各聚类中对象的均值所获得一个“中心对象”（引力中心）来进行计算的。-K-means algorithm accept parameter k Then will the n of prior input data object is divided into k clustering to make won clu
Combining-face-detection-and-people-tracking-in-v
- Face detection algorithms are widely used in computer vision as they provide fast and reliable results depending on the application domain. A multi view approach is here presented to detect frontal and profile pose of people face using Histogram of
vqlbg
- 语音信号处理矢量量化的LBG算法，又称K-mean 算法-in speech signal process vector quantization technology using LBG algorithm with matlab language
yael_kmeans
- 数字图像中快速k均值聚类图像的实现算法，可以运行啊-Fast mex K-means clustering algorithm with possibility of K-mean++ initialization
PAM
- PAM（Partitioning Around Medoid,围绕中心点的划分）算法是是划分算法中一种很重要的算法，有时也称为k-中心点算法，是指用中心点来代表一个簇。PAM算法最早由Kaufman和Rousseevw提出，Medoid的意思就是位于中心位置的对象。PAM算法的目的是对n个数据对象给出k个划分。PAM算法的基本思想：PAM算法的目的是对成员集合D中的N个数据对象给出k个划分，形成k个簇，在每个簇中随机选取1个成员设置为中心点，然后在每一步中，对输入数据集中目前还不是中心点的成员根
k_means_JIT
- k-mean聚类算法的MATLAB源代码程序-k-mean clustering algorithm Source code realization in MATLAB
Cpp1
- 距离与相异度，然后介绍一种常见的聚类算法——k均值和k中心点聚类-Distance and dissimilarity, and then introduce a clustering algorithm- k mean and k-medoids clustering
kmeans1
- kmeans ieee paper , A system for analyzing student’s results based on cluster analysis and uses standard statistical algorithms to arrange their scores data according to the level of their performance is described. K-mean clustering algorithm for a
DM_YeDan
- KNN(K最近邻)分类算法以及K-means(K均值)聚类算法是应用广泛的两种算法。本代码是在VS2010环境下，用 C++语言在基于KNN及K-means算法下，实现了对Iris数据集的分类与聚类。-KNN (K nearest neighbor) classification algorithm, as well as K-means (K mean) clustering algorithm is widely used two algorithms. The code VS2010 en
PatternRecognition
- 模式识别中的k-mean和isodata算法的简单实现-k-mean and isodata algorithm in pattern recognition
K_MEAN
- 数据挖掘经典算法实现 （C语言版）K-MEAN的demo-Data mining the Classic algorithm (C language version) K-Mean demo
New-folder
- Brain Tumor Segmentation Calculation in Brain MR Images using K-Mean Clustering and Fuzzy C-Mean Algorithm
9552010E202
- This paper presents a new cluster validity index for nding a suitable number of fuzzy clusters with crisp and fuzzy data. The new index, called the ECAS-index, contains exponential compactness and separation measures. These measures indicate ho