搜索资源列表
kMean
- Spatial registration of multidate or multisensor images is required for many applications in remote sensing, such as change detection, the construction of image mosaics, DEM generation from stereo pairs, and orthorectifi cation. Registration
Kmeans
- a kmean clustering prog
kmean-(2)
- data mining kmean algorithm
kmean
- 使用k均值方法实现对不同状态下的数据分类。-Use the k-means approach to data classification of different states.
kmeaan
- fire detection using kmean
Test_Kmeans
- Java中实现的 K平均算法 附带注解-Kmean by Java
Kmean
- K均值聚类的源代码，而且是采用ＶＣ＋＋来完成，效果不错，欢迎大家下载-K-means clustering source code, and using VC++ to complete, good results are welcome to download
kmean
- kmeans程序，中有小例子，简单实现的小程序-kmeans program, there is a small example, a simple small program to achieve
KmeansCluster
- 比较方便的KMEANS聚类分析算法MATLAB代码，KMEAN-CLUSTER-Cluster analysis KMEANS better MATLAB code, KMEANS-CLUSTER
KMean
- K均值分类程序K均值进行影像分类，遥感影像分类的方法有很多，如神经网络分类，C均值分类等，K均值分类是其中的一种。-Image Classification
ISODATAaKMEAN_11.22
- IOSDATA & kmean的模式识别分类的实现代码，属于原代码，编程语言MATLAB。-The realization of iosdata&Kmean pattern recognition classification code, belonging to the original code, the programming language is matlab
kfuzzy-master
- Kmean Fuzzy clustering algorithm
kmean--
- 聚类算法在模式识别中有很重要的应用，利用matlab程序编写聚类算法，实现分类功能。-Clustering algorithm has a very important application in pattern recognition. Using matlab program to write clustering algorithm to realize classification function.
SelfKmeansMethod
- 实现数据挖掘中的Kmean算法，先确定其中心店，然后再进行计算每个数据点离簇中心的距离，选择其最小的，然后进行迭代-Kmean data mining algorithms to determine its center shop, and then to calculate the distance of each data point the center of the cluster, its minimum, and then iterate
kmean
- 图像聚类，应用了kmean算法，即k均值聚类，算法的聚类中心应用随机起点法-cluster of picture
ffcmw
- clustering toolbox. fast color kmean
work20180228
- 通过lsh-cd生成不同情景，并使用k均值筛选代表性情景，(Scenario generation, K mean screening representative scenarios)
kmean 聚类算法
- 机器学习中所运用的kmean聚类算法，聚类算法可运用于生物信息学等多个跨学科领域中(Kmean clustering algorithm used in machine learning. Clustering algorithm can be applied to many interdisciplinary fields such as bioinformatics.)