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基于matlab的K均值聚类程序
- 基于matlab的K均值聚类程序。其中用IRIS数据进行验证，得到了很好的结果。文件中包含了演示后的结果图,Matlab-based K-means clustering procedure. Which use IRIS data verification, have had good results. File contains the results of the demonstration plan
k-means-and-cure-in-Iris-Data-Set
- 聚类算法实验,采用两种不同类型的聚类算法:基于划分的聚类方法k-means和基于层次的聚类方法CURE，采用的数据集是：Iris Data Set，数据集中共包含150组数据信息。 材料中有详细的说明文档，具体介绍了算法实现的细节，很容易理解-Clustering algorithm experiment, using two different types of clustering algorithm: Partition-based clustering method k-means
k-means-iris
- 针对著名的UCI机器学习数据库中的iris data的kmeans聚类分析程序，具有代表性-For the well-known UCI machine learning repository of the iris data of kmeans cluster analysis procedure, a representative
Ckmedoids
- K-mean算法，并通过了IRIS数据的测试。-K-mean algorithm, and through the IRIS data testing.
Pattern-Recognition
- 字符识别、K-L人脸识别、虹膜识别、笔画识别等模式识别的例程和资料。-Character recognition, KL face recognition, iris recognition, stroke recognition, pattern recognition routines and information.
Density_Estimation
- 分别采用GMM和KDE对Iris数据集进行密度建模，并进行对比。通过EM算法来确定GMM参数，通过交叉验证来确定K值-GMM and KDE respectively Iris data set of density modeling, and compared. GMM by EM algorithm to determine the parameters of K determined by the value of cross-validation
K-mean
- 最近邻分类器是一个用来聚类的算法，可以用来对iris数据进行聚类-k-means is a neanest alogorim
K近邻法
- K近邻法对Iris数据分类，输入分类结果和准确率。-K-nearest neighbor method for Iris data classification, enter the classification results and accuracy.
k-iris
- 模式识别中用于完成数据的分类而用到的一种方法-k近邻。是将已有数据划分到3个类中，本方法中解决数据Iris数据的划分问题。将150个4维数据划分到3类。K近邻法是求最近的K个元素从而将其划分到已有类中。-Pattern recognition for the completion of the classification of the data used in a way-k neighbors. The existing data are divided into three classes
k-meas
- k近邻法分类iris数据。iris数据共分三类，每一类50个数据，这里把每一类前20个作为训练样本，后30个作为测试样本-k-nearest-neighbor classification iris data. iris data is divided into three categories, each category of data from 50, as the training samples in each category of the top 20 after 30 as th
k-means-for-iris
- 利用K均值聚类对鸢尾花样本进行聚类的matlab程序，包含源代码、样本数据、聚类结果-The use of K-means clustering method to classify iris sample matlab program, the program includes source code, sample data, the classification results
k-means-bayes-algorithm
- k-means和贝叶斯算法对iris数据进行测试-k-means algorithm and Bayesian test for iris data
K-means-IRIS
- 用平均值取代表点的方法和K近邻法对Iris花进行分类-With the average of the representative point method and K-nearest neighbor to classify Iris flower
K-Means-master
- 模糊C均值聚类算法的PYTHON实现，在UCI的IRIS数据集上实现-Fuzzy C-means clustering algorithm PYTHON realization, implemented on UCI s IRIS data set
iris
- 利用机器学习库sklearn库中的k聚类算法进行分类绘图-Machine learning library sklearn library k clustering algorithm to classify and drawing
K均值对iris数据集聚类
- k-means算法对irisdata数据集进行聚类(The k-means algorithm clustering the irisdata datasets)
irisdatasetclustering
- IRIS DATA SET CLUSTERING IN MATLAB
k-means算法的Matlab实现以及Iris数据集
- k-means算法实现以及Iris数据集(Implementation of K-means algorithm and Iris data set)
K-means聚类
- 应用K-means聚类算法，实现对iris数据集的分类(Using K-means clustering algorithm to realize the Classification of iris dataset)
k-means-for-iris
- 利用K均值聚类对鸢尾花样本进行聚类的matlab程序，包含源代码、样本数据、聚类结果(The matlab program of clustering iris samples by K-means clustering, including source code, sample data and clustering results)