搜索资源列表
K-junzhi
- 这是K均值聚类算法的程序,应用相当广泛的,比如在模式识别里就很重要.-This is the K-means clustering algorithm, application fairly extensive, such as pattern recognition is important to Lane.
2655143923
- 此程序是在VC环境下实现k-means均值聚类算法-this procedure is in VC environment to achieve k-means clustering algorithm Mean
K-meaneuclideandistance
- 这个是采用欧氏距离下的K-means算法的matlab实现-this is the Euclidean distance between the K-means algorithm to achieve the Matlab
fk-means
- 数据挖掘中模糊k均值算法，matlab工具编写。-data mining fuzzy k-means algorithm, Matlab tool for the preparation.
kmean
- 这是模式识别中关于k均值动态聚类算法的matlab源码-This is the pattern recognition on the k-means clustering algorithm Matlab FOSS
UseMatlabandCtorealizeKmeans
- 这是一个用c和matlab编写的程序，用于实现k-means算法
amodifiedKmeans
- An Efficient K-Means Clustering Algorithm.
kmeans
- k-means源码(K均值聚类算法源码)
k_means
- K-MEANS算法: k-means 算法接受输入量 k ；然后将n个数据对象划分为 k个聚类以便使得所获得的聚类满足：同一聚类中的对象相似度较高；而不同聚类中的对象相似度较小。聚类相似度是利用各聚类中对象的均值所获得一个“中心对象”（引力中心）来进行计算的。 k-means 算法的工作过程说明如下：首先从n个数据对象任意选择 k 个对象作为初始聚类中心；而对于所剩下其它对象，则根据它们与这些聚类中心的相似度（距离），分别将它们分配给与其最相似的（聚类中心所代表的）聚类；然后再
KC.tar
- 是K均值算法的一个Linux下的编译的程序,用标准C++编写的-K-means algorithm is a Linux compiler procedures used to prepare the standard C
Speaker-Recognition
- code for speaker recognition using k-means clustering
Image-Classification-VCPP-Programme
- 用C++语言编写的MFC程序，用K均值和ISODATA算法实现BMP影像的自动分类。提供良好的交互接口，用户可在图像上选择初始聚类中心和设定分类相关参数。适合作为初学者学习分类算法和MFC编程的参考资料。提供了文档说明程序的操作过程。-MFC program with C++ language, K-means and ISODATA algorithm to achieve the automatic classification of BMP images. Provide a good i
Untitled2
- this is to k means clustering technique
kmeans
- K-Means image Processing
Code
- SVM、Adaboost、K-Means 分类算法的实现，有助于理解分类算法的原理。-SVM, Adaboost, K-Means classification algorithm, helps to understand the principles of classification algorithms.
Untitled2
- color based segmentation using k means clustering
Matlab_code_Q-VMP
- Compressive sensing is the reconstruction of sparse images or signals very few samples, by means of solving a tractable optimization problem. In the context of MRI, this can allow reconstruction many fewer k-space samples, thereby reducing sc