搜索资源列表
聚类k-means
- 一个非常简单的kmeans算法,主要用于聚类分析,用户仅需要输入聚类数(A very simple kmeans algorithm, mainly for clustering analysis, users only need to enter the number of clusters)
DBSCAN
- DBSCAN(Density-Based Spatial Clustering of Applications with Noise)是一个比较有代表性的基于密度的聚类算法。与划分和层次聚类方法不同,它将簇定义为密度相连的点的最大集合,能够把具有足够高密度的区域划分为簇,并可在噪声的空间数据库中发现任意形状的聚类。(DBSCAN is a representative density based clustering algorithm. Unlike the partition and hie
chameleon
- 一段修改后的变色龙聚类算法,可用于无监督聚类。(A modified chameleon clustering code, using matlab.)
WFCM
- 模糊聚类算法,用于对摩多从数据进行聚类,亲自测试还是有点用的(Fuzzy clustering algorithm for clustering data from the multi Yin, personally tested, or a little useful)
gclust
- 聚类算法中gclust算法的代码,供大家参考(Clustering algorithm, gclust algorithm code, for your reference)
PHA_Clustering
- 聚类算法中PHA_Clustering算法的代码,供大家参考(Clustering algorithm, PHA_Clustering algorithm code, for your reference)
基于遗传模拟退火算法的聚类算法
- 基于遗传模拟退火算法实现数据的聚类,为后续的数据分析做准备(Data clustering analysis based on genetic simulated annealing algorithm)
6.聚类和EM算法
- 聚类和EM算法实例,包括线性分类和非线性分类,线性回归和非线性回归(Examples of clustering and EM algorithm include linear classification and nonlinear classification, linear regression and nonlinear regression)
层次聚类代码
- matlab实现层次聚类法,不是用库函数实现的,而是一步步根据算法原理完成的(Matlab hierarchical clustering method, not achieved by library functions, but a step by step according to the principles of the algorithm)
k_medoids
- 采用MATLAB实现k-medoids聚类算法(Implementation of k-medoids clustering algorithm using MATLAB)
knnclassification
- 实现KNN聚类 聚类算法 最简单又实用的聚类方法 常用 适用于多领域(KNN clustering algorithm to achieve the simplest and practical clustering method, commonly used in many fields)
em聚类
- em算法指的是最大期望算法(Expectation Maximization Algorithm,又译期望最大化算法),是一种迭代算法,用于含有隐变量(latent variable)的概率参数模型的最大似然估计或极大后验概率估计。(Expectation Maximization Algorithm use for clustering)
SSC
- 基于稀疏表征的子空间聚类算法,是vidal的算法的MATLAB实现(The subspace clustering algorithm based on sparse representation is the MATLAB implementation of vidal's algorithm)
K_Means
- K-Means是聚类算法中的一种,其中K表示类别数,Means表示均值。顾名思义K-Means是一种通过均值对数据点进行聚类的算法。K-Means算法通过预先设定的K值及每个类别的初始质心对相似的数据点进行划分。并通过划分后的均值迭代优化获得最优的聚类结果。(K-Means is one of the clustering algorithms, in which K represents the number of classes, and Means means the mean. As t
meanshift
- 一个比较简单的meanshift聚类算法(A relatively simple meanshift clustering algorithm)
Clustering-master
- 超级强大的聚类算法+详细的程序说明; Kmeans聚类+ISODATA聚类算法;(Super powerful clustering algorithm + detailed program descr iption; Kmeans clustering +ISODATA clustering algorithm;)
SOMPY-master
- som自组织神经网络聚类算法的python实现(Implementation of SOM clustering algorithm based on Python)
k-means
- 实现k-means聚类算法,里面有数据可以作为测试(This file is use to achieve k-means clustering algorithm.There are data can be used as a test.)
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)
基于 K-means 聚类算法的图像区域分割
- 图像处理领域,基于 K-means 聚类算法的图像区域分割(Image processing domain, image region segmentation based on K-means clustering algorithm)