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数字图像处理alpha版
- 本软件是由作者经过数字图像处理课程的学习，采用vc++.net将其基本算法实现，其算法主要包括： 1.点运算（灰度直方图，直方图均衡处理，线性运算，二值化，灰度化等） 2.几何运算（旋转，放缩，镜像，平移） 3.几何空间增强（均值，中值滤波器，k近邻均值，中值滤波器，Roberts,sobel,priwitt,laplacian,wallis锐化算子等） 4.频率域增强（基2FFT进行空间域到频率域的转换，高斯，理想，巴特沃斯高低通滤波器） 5.形态学（膨胀，腐蚀，开，闭运算，边缘提取） 6.图
kmeans_1
- RBF神经网络的K均值算法，C程序的，供大家参考！-RBF neural network algorithm mean K, C procedures, for your reference!
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
EM_GM
- ％ EM algorithm for k multidimensional Gaussian mixture estimation ％ ％ Inputs: ％ X(n,d) - input data, n=number of observations, d=dimension of variable ％ k - maximum number of Gaussian components allowed ％ ltol - percentage of the log likeli
convolutiondecode
- ％ decode with soft-input viterbi algorithm 硬判决 ％ //k=4,r=1/2 ％输入数据为软信息，并且数据为均值为1的BPSK调制，如果均值为MEAN,那么62,63,103,104行应做相应修改
PAA
- 基于PAA的分段线性表示算法:用等宽度窗口分割时间序列，每个窗口内的时间序列用窗口平均值来表示，就得到了时间序列的一种分段线性表示，它的输入参数为分段数，记为K.-PAA-based algorithm for piecewise linear representation: split time series with windows of same width , use the mean of time series in the window to express, it has bee
k_means
- 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。-In statistics and machine learning, k-means clustering is a method of cluster analysis which aims to partition n observations into
k_meansc_meansCluster
- 基于k均值、c均值等聚类算法，应用于数据挖掘-Based on the mean k, c means clustering algorithm, etc., used in data mining
k_means
- In statistics and machine learning, k-means clustering is a method of cluster analysis which aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean. It is similar to the expectation-max
KMean
- KMEAN C# In data mining, k-means clustering is a method of cluster analysis which aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean. This results in a partitioning of the data sp
Km
- In data mining, k-means clustering is a method of cluster analysis which aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean. This results in a partitioning of the data space into Vo
Kmeans
- 使用Java实现K-means（C均值）聚类算法-Using Java to achieve K-means (C mean) clustering algorithm
k_means
- k均值聚类算法，使各个样本与所在类均值的误差平方和达到最小，并且附有显示程序-k-means clustering algorithm, where the class so that each sample and the mean squared error to a minimum, and with the display program
kmeans1
- K-means算法，算法步骤如下： Step1.利用式(2)计算距离矩阵D=()，其中=dist[i, j] ()； Step2.扫描坐标距离矩阵D，寻找距离的最大值和最小值，用式(3)计算limit； Step3.扫描坐标距离矩阵D，寻找矩阵中距离最小的2个数据a，b，将数据a，b加入集合，={a,b}，同时将数据a，b从U中删除，更新距离矩阵D； Step4.利用 (4)式在U中寻找距离集合最近的数据样本t，如果小于limit，则将t加入集合，同时将t从集合U中删除，更新
K_average
- K-均值聚类算法,基本算法代码，算法的目的是使各个样本与所在类均值的误差平方和达到最小。-The purpose of K-means clustering algorithm, the basic algorithm code, the algorithm is to make the class where each sample mean squared error is minimized.
km
- 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 后再计算每个所获新聚类的聚类中心（该聚类中所有对象的均值）；不断重复这一过程直到标准测度函数开始收敛为止。一般都采用均方差作为标准测度函数. k个聚类具有以下特点：各聚类本身尽可能的紧凑，而各聚类之间尽可能的分开。 该算法的最大优势在于简洁和快速。算法的关键在于初始中心的选择和距离公式。 -First, choo
FunK_meanPolyD
- K-MEAN聚类算法功能展示，是一个多维的算法(K-MEAN clustering algorithm function display, is a multi-dimensional algorithm)
K_Means
- K-Means是聚类算法中的一种，其中K表示类别数，Means表示均值。顾名思义K-Means是一种通过均值对数据点进行聚类的算法。K-Means算法通过预先设定的K值及每个类别的初始质心对相似的数据点进行划分。并通过划分后的均值迭代优化获得最优的聚类结果。(K-Means is one of the clustering algorithms, in which K represents the number of classes, and Means means the mean. As t
81801236k.matlab
- 利用matlab实现k均值聚类算法，亲自调试通过，对于学习k均值聚类算法有很大帮助(Using MATLAB to achieve K means clustering algorithm, personally debugging through, for learning K mean clustering algorithm is very helpful)
IABC_KMC_test_on_Iris_wine_glass
- 克服K均值聚类算法易受初始聚类中心影响的缺点，优化K均值聚类算法(The K mean clustering algorithm is easily affected by the initial cluster center, and the K mean clustering algorithm is optimized.)