搜索资源列表
clustering
- 一个聚类算法用K-mean处理后迭代，论文发表在PAK
K-Means
- 这是K-neans动态聚类算法的源程序，是人工智能领域很有用的一种聚类方法。-This is K-neans source dynamic clustering algorithm, the field of artificial intelligence are useful in a clustering method.
k-centers
- 不同于k均值聚类的k中心聚类，2007年SCIENCE文章Clustering by Passing Messages Between Data Points 中的方法-Unlike k-means clustering of the k cluster centers, in 2007 SCIENCE article, Clustering by Passing Messages Between Data Points of the Method
textclusterr
- 文档分类，用K均值，加入了K的选择算法，不用人为设定聚类个数-Document classification, using K-means, joined the K of the selection algorithm, not the number of artificial clustering
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
km
- K-mean Clustering Applet shown how k-means clustering work
k_means_cluster
- k均值聚类算法 ，c语言实现 了基于均值的聚类分析，同时增加了多维向量分析功能，使得聚类的收敛速度更快。-k means clustering algorithm, c language implemented based on the mean cluster analysis, while increasing the multi-dimensional vector analysis functions, making the convergence faster clustering.
K-means
- 均值为K的聚类算法，是一种对聚类数据进行的最简单的算法，广泛应用在各种场合中。-K mean clustering algorithm for clustering data is the most simple algorithm, widely used in various occasions.
K-MEANS
- 均值计算方法源码实现：分群的方法，就改成是一个最佳化的問題，換句话說，我們要如何选取 c 个群聚以及相关的群中心，使得 E 的值为最小。 -Method of calculating the mean source implementation: clustering method, based on the best change is a problem, in other words, how do we choose c a center cluster and related g
k_means_JIT
- k-mean聚类算法的MATLAB源代码程序-k-mean clustering algorithm Source code realization in MATLAB
Cpp1
- 距离与相异度，然后介绍一种常见的聚类算法——k均值和k中心点聚类-Distance and dissimilarity, and then introduce a clustering algorithm- k mean and k-medoids clustering
K-mean
- K均值聚类,用于空间区域的自适应划分，用MATLAB软件来实现。-K-means clustering for adaptive division of the region of space, using MATLAB software to achieve.
K-mean
- 聚类算法中的k-means算法，和k-medoids 肯定是非常相似的。k-medoids 和 k-means 不一样的地方在于中心点的选取，在 k-means 中，我们将中心点取为当前 cluster 中所有数据点的平均值。-Clustering algorithm k-means algorithm, and k-medoids certainly very similar. k-medoids and k-means not the same place that the center o
EM
- EM 算法，先K-mean 聚类，然后LGB分裂-EM algorithm, the first K-mean clustering, then LGB split
RBF-k均值聚类
- RBF（径向基神经网络）网络是一种重要的神经网络，RBF网络的训练分为两步，第一步是通过聚类算法得到初始的权值，第二步是根据训练数据训练网络的权值。RBF权值的初始聚类方法较为复杂，比较简单的有K均值聚类，复杂的有遗传聚类，蚁群聚类等，这个RBF网络的程序是基于K均值聚类的RBF代码。(RBF (radial basis function network) is an important neural network. The training of RBF network is divided
七个RBF神经网络的源程序
- 包含了RBF源代码，可以用于RBF神经网络编程，其中包括RBF聚类，K均值聚类等(It includes the RBF source code, which can be used for RBF neural network programming, including RBF clustering, K mean clustering, etc.)
Kmeans
- 可以实现K均值聚类的MATLAB程序。但是有点小问题。(The MATLAB program of K mean clustering can be realized.)