搜索资源列表
kMean
- clustering的经典k-mean算法源程序，VB代码-clustering k-mean algorithm, in VB
Ckmedoids
- K-mean算法，并通过了IRIS数据的测试。-K-mean algorithm, and through the IRIS data testing.
K-mean
- 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-K-means algorithm: the number of a given type of K, will be assigned to N objects of category K go, making the object category similarity between the largest, while the category of the simi
kmean
- 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。-k-means algorithm process as follows: First of all, the object data from the n choose k
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
NormalDistribution
- Intention based scoring (IBS) is a scoring system, its goal is to provide a score that more accurately assesses a student’s ability to solve a composition problem, and therefore assess direct effects of the student s programming ability. This process
200732590038
- 计算K均值分类的方法-Calculate K mean classification method. . . . . . . . . . . . . . . . . .
k-mean
- cluster in data minig
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
cmean
- 对于有缺测值的数据取平均 使用： y=cmean(x,k,c) x为包括有nan的数据，仪器观测中经常会出现 k为对x取第几维方向的平均 c的值可以控制缺测的条件，例如c取50，表示这一维方向上，如果有值的观测超过50个值才取平均，否则取nan；如果c取-5，表示缺测数在5以下时才做平均，否则返回nan-calculate the statistical mean of data but tick off nan Usage y=cmean(x,|k) or y
K_mean_clustering
- this code is about k mean clustering in Matlab
K_MEAN
- 数据挖掘经典算法实现 （C语言版）K-MEAN的demo-Data mining the Classic algorithm (C language version) K-Mean demo
w
- 利用matlab实现下面的问题：其中u(k)和z(k)分别为模型的输入和输出变量；v(k)为零均值、方差为1、服从正态分布的白噪声；为噪声的标准差；利用得到的u(k)和z(k)来辨识模型的阶次和参数。-Using matlab to achieve the following questions: where u (k) and z (k) respectively, the input and output variables of the model v (k) with zero mea
kmeans
- kmeans methode (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)
src
- k-means 算法接受参数 k ；然后将事先输入的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 algorithm accepts parameters k n and the previously input data is divided into k-clustering objects in order to make
PRJ_Final
- K-mean Algorithm Initialisation: set seed points Assign each object to the cluster with the nearest seed point Compute seed points as the centroids of the clusters of the current partition (the centroid is the centre, i.e., mean point, of th
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)
新建文件夹
- K均值算法 简单的模式识别的算法 有详细的代码解释(Kmeans K mean algorithm simple pattern recognition algorithm has detailed code interpretation)