搜索资源列表
KMEANS
- 输入:聚类个数k,以及包含 n个数据对象的数据库。输出:满足方差最小标准的k个聚类。处理流程: (1)从 n个数据对象任意选择 k 个对象作为初始聚类中心. (2)根据每个聚类对象的均值(中心对象),计算每个对象与这些中心对象的距离;并根据最小距离重新对相应对象进行划分;(3)重新计算每个(有变化)聚类的均值(中心对象) (4)循环(2)到(3)直到每个聚类不再发生变化为止-Input: number of clusters k, and n data object contains a
K_CenterPoint_PAM
- k中心点算法,也就是PAM算法。是数据挖掘中聚类分析的一种手段,用途广泛。-k center algorithm, i.e. PAM algorithm. Data mining is a means of cluster analysis, and versatile.
zkmeans
- Cluster Scheme using K-means
Kmeans
- 用K-means算法对数据进行聚类分析,得到不同K值情况下的聚类结果并绘制出了J-K关系图。-The K-means algorithm is used to cluster the data, and the clustering results are obtained under different K values. The J-K relation graph is drawn.
85375535Kmeans
- K均值聚类算法是先随机选取K个对象作为初始的聚类中心。然后计算每个对象与各个种子聚类中心之间的距离,把每个对象分配给距离它最近的聚类中心(K means clustering algorithm is first randomly selected K objects as the initial clustering center. Then calculate the distance between each object and each seed cluster center and
kmeans
- 可以直接拿来用 python2.7 在数据挖掘中,K-Means算法是一种 cluster analysis 的算法,其主要是来计算数据聚集的算法,主要通过不断地取离种子点最近均值的算法。(In the data mining, K-Means algorithm is a cluster analysis algorithm, which is mainly to calculate the data aggregation algorithm, mainly through the con
KMeans
- K-means算法是将样本聚类成k个簇(cluster)。(The K-means algorithm is to cluster the samples into k clusters.)
cskmeans
- k均值聚类 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。(kmeans cluster K means clustering algorithm accepts parameters K; N data object classification and the previously inpu
彩色图像分割
- The input color image will be coarsely represented using 25 bins.Coarse representation uses the spatial information from a Histogram based windowing process. K-Means is used to cluster the coarse image data.
statistics_kmeans
- K-means算法是一种硬聚类算法,根据数据到聚类中心的某种距离来作为判别该数据所属类别。K-means算法以距离作为相似度测度。(kmeans uses the k-means++ algorithm for centroid initialization and squared Euclidean distance by default. It is good practice to search for lower, local minima by setting the 'Replica
kmeans
- K-means算法是集简单和经典于一身的基于距离的聚类算法 采用距离作为相似性的评价指标,即认为两个对象的距离越近,其相似度就越大。 该算法认为类簇是由距离靠近的对象组成的,因此把得到紧凑且独立的簇作为最终目标。(K-means algorithm is a distance based clustering algorithm which is simple and classic. Distance is used as a similarity evaluation index, t
julie
- 基于K-means聚类算法的图像区域分割的程序实现(Cluster the function image.)
聚类分析程序
- 包含了各类聚类分析程序。主要包括系统聚类,基于欧氏距离的聚类,变量系统聚类和K均值聚类(It includes all kinds of cluster analysis programs. It mainly includes system clustering, Euclidean distance based clustering, variable system clustering and K-means clustering)
k_means
- 利用K均值算法对Iris数据集进行聚类,实现Iris数据集的无监督学习。(K-means algorithm is used to cluster iris data set to realize unsupervised learning.)