搜索资源列表
BFO-cluster
- 基本的细菌觅食聚类程序，测试数据集为IRIS数据集。-The basic bacterial foraging clustering procedure, the test data set for the IRIS data set.
Umoshishibies
- 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=xx4=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.-C-means clustering procedure, and the following data and cluster analysis. Confirm the programming is correct, Ca
DMProject
- 数据挖掘算法KNN、K-means的实现与挖掘Iris数据集的结果分析-Data mining algorithms, KNN, the K-means to achieve mining Iris data set
kMeans
- k-means算法,内带iris数据作为例子-k-means algorithm, within with the iris data as an example
BP_IRIS
- BP神经网络实现iris数据分类，，非常的好用啊。。。效果杠杠的-BP neural network to achieve the iris data classification, and very easy to use. . . Leverage
ID3
- 决策树分类算法的实现和性能测试,使用UCI Iris Data Set进行测试。-Implementation and performance testing of the decision tree classification algorithm
c-mean-value
- 学习模式识别时候，会用到C均值处理一些数据。其中处理Iris数据是一个典型的问题，本例子由本人编写，为了完成其数据的分类。总共150个4维数据，将它分为三类。每个数据原归属于三类。-Learning pattern recognition when used C-means to deal with some data. Which deal with Iris data is a typical problem, the examples of which I am writing, in o
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
iris_data_files
- iris data file for using in matlab
mat
- 对iris数据集分类 采用bp网络 利用交叉验证优化参数-Classification of the iris data set bp network use of cross-validation optimization parameters
Kjunzhi
- 模式识别大作业K均值算法matlab平台实现，内有iris数据用于测试一起其他相关资料。-Pattern Recognition great job K-means algorithm matlab platform implemented within the iris data used for testing along with other relevant information.
chimerge_recursiveversion_iris_2
- CHi Merge for Iris (data mining)
isodate
- isodata聚类算法用于聚类类IRIS数据，分类效果较好，运行稳定-isodata clustering algorithm for clustering classes IRIS data classification better, stable operation
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
Cluster
- 使用k-means、k-media、层次聚类方法，并通过iris数据集测试-Using k-means, k-media, hierarchical clustering method, and through the iris data set test
ANN_BP--classifier
- ANNbp对UCI中鸢尾花数据集分类，150做训练样本，剩下150做测试样本-ANNbp on the UCI iris data set classification, 150 training samples, the remaining 150 test samples
iris_classification
- Classification of iris data set using FLANN and epoch based learning(not iterative).The same technique is again used for realizing XOR gate-Classification of iris data set using FLANN and epoch based learning(not iterative).The same technique is aga
KL
- 模式识别里的KL变换的代码，应用Iris数据集实现KL变换算法。-KL transform code, pattern recognition application Iris data set KL transform algorithm.
Bayes-Iris
- bayes分类算法，适合数据挖掘初学者学习-Bayesian classification algorithm for data mining beginners to learn
Cluster
- 对iris数据进行聚类，能够得到每类的具体数据信息-Clustering of iris data, can be specific for each type of data