搜索资源列表
MyKmeans
- 实现聚类K均值算法： K均值算法：给定类的个数K，将n个对象分到K个类中去，使得类内对象之间的相似性最大，而类之间的相似性最小。 缺点：产生类的大小相差不会很大，对于脏数据很敏感。 改进的算法：k—medoids 方法。这儿选取一个对象叫做mediod来代替上面的中心 的作用，这样的一个medoid就标识了这个类。步骤： 1，任意选取K个对象作为medoids（O1,O2,…Oi…Ok）。 以下是循环的： 2，将余下的对象分到各个类中去（根据与medoid最相近的原则）； 3，对于每个类（Oi）
cmeans
- 实现聚类K均值算法： K均值算法：给定类的个数K，将n个对象分到K个类中去，使得类内对象之间的相似性最大，而类之间的相似性最小。-achieving K-mean clustering algorithms : K-means algorithm : given the number of Class K, n objects assigned K to 000 category, making such objects within the similarity between the lar
clustering
- 一个聚类算法用K-mean处理后迭代，论文发表在PAK
MeanShiftCluster.zip
- Mean shift clustering. K means clustering.,Mean shift clustering. K means clustering.
kmeans-image-segmentation
- K-meansK均值聚类在无监督的情况下选择图像特征的算法-K-meansK means clustering in the case of unsupervised image feature selection algorithm
kmeans
- 改进的k-means方法，对聚类的实例节能型加权 少数类多数类的函数-Improved k-means method for clustering a small number of examples of energy-saving type of weighted majority of types of function
kcluster
- 一种基于软划分方法的聚类方法——模糊k均值法聚类分析。-A division of methods based on soft clustering method- fuzzy k-means cluster analysis.
GA1E1
- 用Ｋ均值和遗传算法实现了半监督聚类算法，这是个一个已经发表的论文的源程序-Using K-means and genetic algorithm to achieve a semi-supervised clustering algorithm, this is a paper published source
kmeans
- 程序很好的实现了k均值的聚类，我已经使使用过，效果非常好，很好用-Program achieved a very good k mean clustering, I have so used, the effect is very good, very good use
km
- /K mean clustering algo
engdemo
- the classic pattern recognition algorithms, dynamic clustering algorithm k mean using Matlab programming, as well as classification of the class analysis
Detection-of-viruses-in-tomatoes-leaf-based-on-K-
- Detection of viruses in tomatoes leaf based on K-Mean clustering algorithm
K
- K-MEANS聚类分析，用于实现目标识别效果~识别率较高~-K-MEAN Clustering analysis, used to achieve the target recognition effect ~ recognition rate is higher
Kmeans
- 对已知数据进行k均值聚类，数据保存在txt文件(K mean clustering for known data)
irisdatasetclustering
- IRIS DATA SET CLUSTERING IN MATLAB
RBF-k均值聚类
- RBF（径向基神经网络）网络是一种重要的神经网络，RBF网络的训练分为两步，第一步是通过聚类算法得到初始的权值，第二步是根据训练数据训练网络的权值。RBF权值的初始聚类方法较为复杂，比较简单的有K均值聚类，复杂的有遗传聚类，蚁群聚类等，这个RBF网络的程序是基于K均值聚类的RBF代码。(RBF (radial basis function network) is an important neural network. The training of RBF network is divided
k-means程序
- 介绍了k-means 均值聚类，能很好的将离散的点，聚类成几个指定的聚合点。(The K-means mean clustering is introduced, and the discrete points can be well clustered into several designated aggregation points.)
K-means
- K-means聚类算法的matlab实现(k-means clustering is a method of vector quantization, originally from signal processing, that is popular for cluster analysis in data mining. k-means clustering aims to partition n observations into k clusters in which each obse
Kmeans
- 可以实现K均值聚类的MATLAB程序。但是有点小问题。(The MATLAB program of K mean clustering can be realized.)
49779421k-mean
- k均值聚类程序，虽然matlab中也有自带的，但是这个速度不错。(K mean clustering program, although matlab also has its own, but this speed is good.)