搜索资源列表
kmeans
- C# 聚类k-means 建立tf*idf的聚类过程-C# cluster for every one
juleifenxi
- 基于C#.net实现了聚类分析的K均值算法-Based on C#. Net realized cluster analysis K-means algorithm
Cmeansclusteringmethods
- 本算法在vc++6.0中进行实验。分别就分解聚类和C-均值聚类两种方法在IRIS数据集上进行操作。分类前先将数据集中的样本顺序打乱形成混合数据。分解聚类中,采用前100个样本用对分法编制程序将数据分为两类。C-均值聚类采用全部的150个样本,将类别参数K设为3,将数据分为三类。-The algorithm in vc++6.0 in the experiment. Separate cluster and decomposition of two C-means clustering metho
kmeans
- 使用K-均值聚类算法在IRIS数据上进行聚类分析.-K-means clustering algorithm using IRIS data in the cluster analysis.
k_means
- In statistics and machine learning, k-means clustering is a method of cluster analysis which aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean. It is similar to the expectation-max
KMEANS
- k-means C++ 源代码, 修正原来的错误, 增加的新功能 1、用vector实现其存储 2、直接在程序中读取数据集 3、结果可以保存到文件中 4、用户可以输入聚类个数 5、初始聚类中心随机选择(代码自动随机)-k-means C++ source code, fixes the original error, the increase in new features 1, 2, with the vector to achieve its store dire
KMeans
- K-means algorithm which is used to find cluster given a dataset
jz
- 利用K-均值聚类算法对如下数据进行聚类,要求输出每个类及其中的元素-K-means clustering algorithm using the following data on the cluster, requiring the output of each class and its elements
Harris
- 研究一种红外医学图像处理与分析方法,实现红外人脸图像中特征区域的自动定位。方法 针对红外正面脸部图像,采用一种无监督的局部和全局的特征提取方法,首先通过阈值法区分出前景和 背景,并根据面部特征对称性在前景中确定鼻区 然后在面部确定一个包含所有特征的矩形区域,利用 Harris算子在该区域检测出角点,并找出这些点的局部最大值点 最后用K-means方法对这些点进行 聚类 -To develop an mi age analyzing procedure forautomatic
Kmeans
- 基于数据挖掘的k-means算法,成功实现簇类更新-Based on Data Mining k-means algorithm, cluster classes successfully updated
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,
1
- 聚类分析(K-Means)程序实现及展现(-Cluster analysis (K-Means) to achieve and demonstrate procedures (
KMeansCluster
- k均值聚类的VC++实现,适合语音等方面聚类之用-k means clustering of VC++ implementation of voice, etc. for use in cluster
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 takes parameters k and then advance the input data object is divided into n-k-clustering in order to make
fkm
- k-means算法IDL语言的实现,用于对图像的聚类分析等-k-means algorithm for the realization of IDL language for cluster analysis of image
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
Fuzzy-neural1
- 首先要对样本进行聚类分析,以此来确定模糊规则个数。利用K-means法对样本聚类。-First cluster analysis of samples, in order to determine the number of fuzzy rules. Method using K-means clustering of the sample.
Kjulei
- 一种K 均值的算法,是老师布置的作业,效果还不错,对IARS花聚类分析-One kind of K-means algorithm, is the teacher assignments, the results were good, the flower cluster analysis IARS
kmeans
- function [L,C] = kmeans(X,k) KMEANS Cluster multivariate data using the k-means++ algorithm. [L,C] = kmeans(X,k) produces a 1-by-size(X,2) vector L with one class label per column in X and a size(X,1)-by-k matrix C containing the centers
kmeansK
- KMEANSK Performs K-means clustering given a list of feature vectors and k The argument k indicates the number of clusters you want the data to be divided into. data_vecs (N*R) is the set of R dimensional feature vectors for N data points. E