搜索资源列表
K-mean
- 聚类算法中的k-means算法，和k-medoids 肯定是非常相似的。k-medoids 和 k-means 不一样的地方在于中心点的选取，在 k-means 中，我们将中心点取为当前 cluster 中所有数据点的平均值。-Clustering algorithm k-means algorithm, and k-medoids certainly very similar. k-medoids and k-means not the same place that the center o
k-means
- K均值算法，将数据矩阵命名为data，设置聚类簇个数k，可对多维数据进行聚类。-K mean algorithm, the data matrix is named data, set the number of clusters K, can be used to cluster the multi-dimensional data.
K-MeansPP
- K均值算法的实现，其中实现随机获取聚类中心-Realizing the K-means algorithm, which realize stochastic get clustering center
RBF-k
- RBF-k均值聚类算法的matlab程序和样本数据，可用于RBF-k均值聚类算法的仿真。-RBF-k-means clustering algorithm matlab program and sample data, can be used to simulate the RBF-k-means clustering algorithm.
k
- 用K均值聚类分析把多组数据分成两类 本程序为给定20组数据（用矩阵A表示）分成B、C两组。-K-means clustering analysis of the multiple sets of data into two categories This program is given 20 sets of data (represented by the matrix A) into B, C groups.
k-means
- matlabk-means聚类多维数据分析-matlabk-means clustering multidimensional data analysis
K-means-IRIS
- 用平均值取代表点的方法和K近邻法对Iris花进行分类-With the average of the representative point method and K-nearest neighbor to classify Iris flower
k-means_segmention
- 基于K均值聚类的彩色图像分割算法，最简单的聚类算法，可以看一下-K-means clustering segmentation algorithm based on the color image, the most simple clustering algorithm, can look
K-Means-master
- 模糊C均值聚类算法的PYTHON实现，在UCI的IRIS数据集上实现-Fuzzy C-means clustering algorithm PYTHON realization, implemented on UCI s IRIS data set
k
- 主要是K-Means算法的实现，可以直接执行，其中图片的载入用到opencv，其他均为C++源码。-Mainly K- Means algorithm implementation, which can be d directly, the images load use opencv, other are c++ source code.
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
k-value-and-cells-counting
- K均值聚类、分水岭算法,可以用来白细胞红细胞计数-K-means clustering, watershed algorithm, can be used for white blood cell red blood cell count
K-meansbased-on-image-segmentation
- 基于K-means聚类算法的图像分割 代码及实验测试结果-K-means clustering algorithm based on image segmentation code and some experimental results
k-junzhi
- 通过对K-均值算法的编程实现，加强对该算法的理解和认识。提高自身的知识水平和编程能力，认识模式识别在生活中的应用。 算法思想K-均值算法的主要思想是先在需要分类的数据中寻找K组数据作为初始聚类中心，然后计算其他数据距离这三个聚类中心的距离，将数据归入与其距离最近的聚类中心，之后再对这K个聚类的数据计算均值，作为新的聚类中心，继续以上步骤，直到新的聚类中心与上一次的聚类中心值相等时结束算法。-By programming K- means algorithm implementation, s
k
- k-means实现聚类算法的程序，简单易懂，适合初学者-K-means clustering algorithm to achieve the procedure, easy to understand, suitable for beginners
RBF_K-means
- 考虑Hermit多项式的逼近问题 ，用k-means训练RBF网络-Consider Hermit polynomial approximation problem with k-means training RBF network
K-means_C
- 用K-means算法实现数据聚类。首先利用C随机产生800个数据，并将这800个数据作为一组训练样本；其次利用K-means的原理跟方法将这组样本聚类成8个类，从而实现数据的分类。-Data Clustering with K-means Algorithm. First, 800 data were randomly generated by C and the 800 data were used as a set of training samples. Secondly, the K-m
k-means1
- Python version of k-means for data clustering
k-means
- 简单实现聚类算法中的经典k-menans算法，实现数据是二维数据- U7B80 u5B5 u5B9 u7B0 u803A u7R09 u7B09 u7B09
Classification K-NN
- classification des image satilitaire par k-means