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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
K-Means.java K-means分群法
- K-Means.java K-means分群法(组数数据皆不固定，以读入档案， iris.txt)-K-Means.java K-means grouping method (group, none fixed the number of data in order to read the file, iris.txt)
Ckmedoids
- K-mean算法，并通过了IRIS数据的测试。-K-mean algorithm, and through the IRIS data testing.
ex-12
- K-Means.java K-means分群法(組數資料皆不固定，開小視窗選檔案:A or B or iris，10 runs + output至Excel)-K-Means.java K-means grouping method (the number of data groups, none is fixed, to open a small window, select the file: A or B or iris, 10 runs+ output to Excel)
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
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.
K-MEANS
- 该文档主要讲述的是K-means算法分析Iris数据集-The document covers the K-means algorithm for Iris data set
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
- K邻近算法的实现，解决IRIS问题，经过更改可解决WINE问题-To K neighboring algorithm, to solve the the IRIS problem, after change resolves WINE
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
iris-k-nn
- Iris 是一种鸢尾属植物。在数据记录中，每组数据包含Iris花的四种属性：萼片长度，萼片宽度，花瓣长度，和花瓣宽度，三种不同的花各有50组数据. 这样总共有150组数据或模式。这里用K近邻法进行分类。-Iris is a genus Iris. In the data recording, the data containing each of the four attributes Iris Flower: sepals length, sepal width, petal length,
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)
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)