搜索资源列表
secai
- 简单的K均值聚类法，本文中选用了5个聚类中心点，具体的聚类中心个数可以参考灰度直方图来选取-Simple K-means clustering method, this paper has selected five cluster center, the specific number of cluster centers can refer to the histogram to select
Em
- 使用k均值算法计算聚类的重心，并用EM算法计算各聚类的参数-Using k-means clustering algorithm to calculate the center of gravity, and using EM algorithm to calculate the parameters of each cluster
KmeansCluster
- K均值聚类,自编的能够运行的K均值聚类程序，可用于聚类分析-K-means clustering，Self capable of running K-means clustering procedure that can be used cluster analysis
K_KMeans
- 使用k-means算法对未知协议帧的聚类-use kmeans algorithm to cluster
fun_traindic_kmeans
- kmeans 聚类算法，主要用来对一堆数据形成他们的码本-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 ea
kmanes2
- k means clustering algorithm new 2 cluster
Fk-menas
- 基于Hadoop的模糊K-Means算法，在MapReduce框架下编写，经集群测试成功运行。压缩包中包含源码和实验数据-Hadoop-based fuzzy K-Means algorithm, written in the MapReduce framework, through the cluster test run successfully. Compressed package contains the source code and experimental data
Mykmeans
- 改进的K-means算法，速度快，输入是样本的特征向量和聚类类别数，每一行代表一个样本，每一列代表一个特征，输出聚类标签-Improved K-means algorithm, fast, is a sample of the input feature vectors and cluster number of categories, each row represents a sample, and each column represents a characteristic output
ruqinjiance
- 使用K-Means算法对KDD Cup 1999数据集进行聚类分析，建立简单的入侵检测模型；利用入侵检测模型对测试数据进行预测-K-Means algorithm using KDD Cup 1999 data sets cluster analysis, create a simple intrusion detection model use of intrusion detection model to predict the test data
k12
- 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。-K- means algorithm accept input k Then could be divided into k n data object clustering in order to make the clustering obtained
kmeans
- k-means 算法用java实现对二维点集合的分类 输入相应的类别数 选择聚类中心-k-means algorithm to classify the input using java-dimensional set of points corresponding to the number of categories to select the cluster centers
K_mean
- k均值代码，简单易懂，鼠标右键选择聚类中心-k-means code, easy to understand, right mouse button to select the cluster center
K_Means
- 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。一般都采用均方差作为标准测度函数. k个聚类具有以下特点：各聚类本身尽可能的紧凑，而各聚类之间尽可能的分开。下面给出我写的源代码。-work process k-means al
kmeansPP
- 一个有效的实现k - means+ +聚类多元数据算法。已经证明,这种算法有一个预期值的上界值。此外,k - means + +具有较好的收敛性。-An efficient implementation of the k-means++ algorithm for clustering multivariate data. It has been shown that this algorithm has an upper bound for the expected value of the
Ant-clustering-algorithm-with-K-harmonic-means-cl
- This paper tell about hybrid ant colony- kmeans algorithms. Clustered by ant colony is used for initial cluster for kmean algorithm.
KMeans
- K-均值聚类算法，属于无监督机器学习算法，发现给定数据集的k个簇的算法。 首先，随机确定k个初始点作为质心，然后将数据集中的每个点分配到一个簇中，为每个点找距其最近的质心， 将其分配给该质心对应的簇，更新每一个簇的质心，直到质心不在变化。 K-均值聚类算法一个优点是k是用户自定义的参数，用户并不知道是否好，与此同时，K-均值算法收敛但是聚类效果差， 由于算法收敛到了局部最小值，而非全局最小值。 K-均值聚类算法的一个变形是二分K-均值聚类算法，该算法首先将所有点作为一个簇，然
Kmeans
- 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。-k-means algorithm accepts input k then n data objects into k clusters in order to make clustering satisfy obtained: the objects i
ColorTrain
- 从图像数据集中随机采样得到rgb集合，并使用k-means聚类训练RGB颜色码本，作为颜色特征-Get the RGB color features from the image dataset, and use k-means algorithm to get the RGB cluster centers which we could use to be color codebook
Chinese-(1)
- K-MEANS algorithm Input: cluster number k, and contains n data object . Output: the minimum
dbscan-721ea2b3e634.tar
- K-MEANS algorithm Input: cluster number k, and contains n data object . Output: the minimum