搜索资源列表
IRIS数据
- IRIS数据 用于聚类方法 主要用于模式识别、图像分割等-IRIS data for clustering method used pattern recognition, image segmentation, etc.
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
moshishibie
- 先用C-均值聚类算法程序，并用下列数据进行聚类分析。在确认编程正确后，采用蔡云龙书的附录B中表1的Iris数据进行聚类。然后使用近邻法的快速算法找出待分样本X（设X样本的4个分量x1=x2=x3=x4=6；子集数l=3）的最近邻节点和3-近邻节点及X与它们之间的距离。-First C-means clustering algorithm procedures and with the following data for cluster analysis. After confirming t
Cluster
- 使用分解聚类算法在IRIS数据上进行聚类分析，IRIS数据是由鸢尾属植物的三种单独的花的测量结果所组成，模式类别数为3，特征维数是4，每类各有50个模式样本，总共有150个样本。-The use of decomposition in the IRIS data clustering algorithm on the cluster analysis, IRIS data are from the iris flower three separate components of the meas
C-means
- 使用c-均值聚类算法在IRIS数据上进行聚类分析，随机选择三个初始聚类中心，经过多次迭代，最终将150个样本分为三类。-Use c-means clustering algorithm in the IRIS data on the cluster analysis, three randomly chosen initial cluster centers, through a series of iterative, 150 samples will eventually fall into
subtractive
- subtractive clustering
1
- Iris segmentation using fuzzy clustering and DTCWT
irisfcm
- This illustrates how to use Fuzzy C-Means clustering for Iris dataset.
iso data
- ISODATA聚类算法，内附详细注解，使用了Iris数据集。ISODATA是一种常用的聚类算法之一。-ISODATA clustering algorithm, containing detailed notes, using the Iris data set. ISODATA clustering algorithm is a common one.
JMinHEP-1.0.tar
- clustering iris data java
iris
- 用自组织特征映射神经网络对Iris数据集进行分类，我用神经网络工具箱编写了个简单的程序，实现iris的分类。-Using self-organizing feature map neural network examples of clustering Iris data set classification, I use neural network toolbox to write a simple program, realize the classification of Iris.
fcm-test
- 模糊聚类iris标准数据测试。。。采用fcm方法。-Fuzzy clustering iris standard test data
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
improved-fuzzy-c-means-clustering
- 该算法引入遗传算法对模糊c均值算法进行改进，并在iris数据集中进行实验验证，得到很高的正确率。-The algorithm genetic algorithm fuzzy c-means algorithm is improved, and focus on experiments in the iris data to obtain a high accuracy.
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
ABC-clustering-with-centroid-representation
- Implemantation of ABC algorithm for data clustering with centroid representation of solutions, and graphic representation of iris data set
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-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)