搜索资源列表
SimpleKMeans
- 数据挖掘中经典的k means聚类算法实现-kmeans cluster
Kmeans.java.tar
- K-Means algorithm to create cluster formation for input files
SC_demo
- 整理图像特征点提取和分类的程序（可以作为场景分类的前期工作），自己调试过能运行，特征点提取用的SIFT算法，使用K-means聚类算法，将得到的20个聚类中心写入txt文本中-Finishing the image feature point extraction and classification procedures (which can be as the preparatory work of the scene classification), their own debugging
philbinj-fastcluster-bf3d361
- 这是一个快速进行kmeans聚类的算法，里面有点乱，对于学习者可以下来看看，对于要进行快速开发者可能用不到。-This book and code is talk about k-means method for cluster.It is usefull for you who want to study the method.Thanks
julei
- 数组聚类分析代码，将数组中的数按照K-MEANS算法聚类-Array cluster analysis code, the array in accordance with the K-MEANS algorithm of clustering
Semi-supervised-learning
- 义了一个欧氏距离和监督信息相混合的新的最近邻计算函数，从而将K一均值算法很好地应用于半 监督聚类问题。针对K一均值算法初始质心敏感的缺陷，用粒子群算法的搜索空间模拟聚类的欧氏空间，迭代搜 索找到较优的聚类质心，同时提出动态管理种群的策略以提高粒子群算法搜索效率。算法在UCI的多个数据集 上测试都得到了较好的聚类准确率。-Righteousness of a Euclidean distance and supervision of a mixture of new nearest n
Kkmeann-
- 使用k-means算法对150个数据集进行分簇。-K-means algorithm uusing 150 data sets to carry out sub-cluster. -Use k-means algorithm on 150 data sets clustering.-K-means algorithm uusing 150 data sets to carry out sub-cluster.
K_means_clustering
- 聚类算法，用于实现多类数据的聚类分析，K-means是其中的一种-Cluster analysis, K-means clustering algorithm, used to implement a variety of data
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
KMEANS
- This directory contains code implementing the K-means algorithm. Source code may be found in KMEANS.CPP. Sample data isfound in KM2.DAT. The KMEANS program accepts input consisting of vectors and calculates the given number of cluster centers u
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
- 利用k-means算法进行聚类，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最有分类，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。-Algorithm using k-means clustering, K-means algorithm Euclidean distance as a similarity measure, it is the pursuit of the vector V corresponding to a initial
Image-Classification-VCPP-Programme
- 用C++语言编写的MFC程序，用K均值和ISODATA算法实现BMP影像的自动分类。提供良好的交互接口，用户可在图像上选择初始聚类中心和设定分类相关参数。适合作为初学者学习分类算法和MFC编程的参考资料。提供了文档说明程序的操作过程。-MFC program with C++ language, K-means and ISODATA algorithm to achieve the automatic classification of BMP images. Provide a good i
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)
Kmeans
- k均值聚类算法，可以有效的找到聚类中心，并且把两类聚在一起，很经典实用。-k-means clustering algorithm, can effectively find the cluster center, and the two categories together, very classic.
k_mean
- 在聚类分析中，K-均值聚类算法（k-means algorithm）是无监督分类中的一种基本方法，其也称为C-均值算法，其基本思想是：通过迭代的方法，逐次更新各聚类中心的值，直至得到最好的聚类结果。 -In cluster analysis, K-means clustering algorithm (k-means algorithm) is unsupervised classification is a basic method, which is also known as C
k_means
- k-means聚类方法 编写k-means聚类方法对这些点进行聚类-k-means clustering method to write k-means clustering method to cluster these points
Kjunzhi
- 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 data mining, k- Means algorithm is a kind of cluster analysis algorithm, the main is to calculate the data aggregation algorithm, mainly through constantly take the seed
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
K_Means_image_compression
- - K means algorithm is performed with different initial centroids in order to get the best clustering. - The total cost is calculated by summing the distance of each point to its cluster centre and then summing over all the clusters.Based on the mi