搜索资源列表
k-mediod-PAM
- k-medoids with matlab is very strong
K-means&DBSCAN
- python实现K-means聚类算法和DBSCAN算法，都是最简单的聚类(Python implements k-means clustering algorithm and DBSCAN algorithm, which are the simplest clustering)
a path with a distance of K and no loop
- 求无向图任意两个顶点是否存在距离为k且不含回路的路径(Whether any two vertex of an undirected graph has a path with a distance of K and no loop)
3-19有向树独立K
- 要求有向树Ｔ的k个独立顶点组成的集合Ｆ，使cost(F)=∑min w(x)*d(x,u)的值达到最小。与有向树k中值问题类似，把有向树变转成为与之等价的二叉树，设Ｔ的以顶点x为根的子树T(x),其左、右儿子顶点分别为y和z。(A set F that consists of a k independent vertex to a tree T is required to minimize the value of the cost (F) = min w (x) *d (x, U). Sim
c# 读取 大智慧 K线 数据FinData1.0
- c# 读取 大智慧 K线 数据FinData1.0(c# framwork4.0 vs202)
Type-K-Thermocouple-Chart-C
- K TYPE THERMOCOUPLE Chart
k-means
- 实现k均值聚类算法，可以使用彩色图像，通过随机初始化聚类中心，完成聚类(The K-means clustering algorithm can use color images to initialize cluster centers randomly and accomplish clustering.)
K-means图像识别
- 利用K-means对图像进行聚类，识别。您可以设置参数达到更好的识别效果(Using K-means to cluster and identify images.You can set parameters to achieve better recognition results)
K---MEANS
- 随机生成1000个二维坐标点并用K-means算法计算聚类结果(1000 two-dimensional coordinate points are generated randomly and the clustering results are calculated by K-means algorithm)
K线蜡烛图
- 将K线信号输出至图标上 可以帮助初学者学习了解k线的基本知识(put the signal on the patten)
k-means聚类算法
- k-means聚类算法的代码实现，只需要更改数据就可以实现，而且有注释，很容易懂(The code implementation of the k-means clustering algorithm can be realized only by changing the data, and there are notes that make it easy to understand)
K近邻算法
- 此处用python实现机器学习k近邻算法(Implementation of k nearest neighbor algorithm for machine learning)
k-means程序
- 介绍了k-means 均值聚类，能很好的将离散的点，聚类成几个指定的聚合点。(The K-means mean clustering is introduced, and the discrete points can be well clustered into several designated aggregation points.)
Calculation-K
- 计算K线形态代码: 返回相应形态的值。例如：光头光脚阳线、光头光脚阴线、下引线阳线、上引线阴线、下引线阳线、上引线阴线、上下引线阳线、上引线阴线等等形态值(Calculate the K line form code)
K+帮助教程-可以发客户
- 金蝶K+辅助文档 ,包括自定义表单，数据库修改，榜单的制作等(Kingdee K+ auxiliary document, including custom form, database modification, list making, etc.)
K-means
- K-means算法是硬聚类算法，是典型的基于原型的目标函数聚类方法的代表，它是数据点到原型的某种距离作为优化的目标函数，利用函数求极值的方法得到迭代运算的调整规则。K-means算法以欧式距离作为相似度测度，它是求对应某一初始聚类中心向量V最优分类，使得评价指标J最小。算法采用误差平方和准则函数作为聚类准则函数。(The K-means algorithm is a hard clustering algorithm, which is representative of the prototy
K-means
- 利用MATLAB实现K均值聚类算法，加深对该算法的理解。(We use MATLAB to achieve K mean clustering algorithm to deepen our understanding of the algorithm.)
k-medoids
- k-medoids聚类算法对数据进行分类处理(k-medoids Clustering algorithm for data classification)
K-means
- K-means聚类算法的matlab实现(k-means clustering is a method of vector quantization, originally from signal processing, that is popular for cluster analysis in data mining. k-means clustering aims to partition n observations into k clusters in which each obse
k均值聚类算法
- 根据k均值聚类的原理，实现一些数字的聚类，但是具体类别数需要自己设置(Clustering of some numbers by K mean clustering)