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precluster-聚类算法
- 非监督聚类算法
模糊C-均值（FCM）聚类算法Matlab实现
- 模糊聚类是一种重要数据分析和建模的无监督方法。简单概述模糊聚类的概念，介绍模糊C-均值（FCM）算法，并在matlab软件上对该算法进行仿真，结果表明，该算法设计简单，应用范围广，但改算法仍存在容易陷入局部极值点等问题，还需进一步研究
基于贝叶斯网络的半监督聚类集成模型
- 已有的聚类集算法基本上都是非监督聚类集成算法,这样不能利用已知信息,使得聚类集成的准确性、鲁棒性和稳定性降低.把半监督学习和聚类集成结合起来,设计半监督聚类集成模型来克服这些缺点.主要工作包括:第一,设计了基于贝叶斯网络的半监督聚类集成(semi-supervised cluster ensemble,简称SCE)模型,并对模型用变分法进行了推理求解;第二,在此基础上,给出了EM(expectation maximization)框架下的具体算法;第三,从UCI(University of Ca
k-meansjava.rar
- 用JAVA语言实现的经典聚类算法k-means，聚类与分类不同，它是无监督的过程，,JAVA language used to achieve a classic clustering algorithm k-means, clustering and classification of different, it is the unsupervised process,
texture-gabor.rar
- gabor提取纹理特征，k-means方法无监督聚类进行图像分割,extract texture feature, cluster by k-means
c_modify
- 在matlab环境下利用c均值聚类方法解决非监督分类问题-Matlab environment in the use of c-means clustering method to solve the problem of non-supervised classification
apcluster
- 无监督聚类算法,能够自动聚类,不必预先给出类数,聚类精度好于常用的聚类算法.-Unsupervised clustering algorithm, can automatically cluster, do not have to give in advance the number of categories, clustering accuracy of better than commonly used clustering algorithm.
kmeans-image-segmentation
- K-meansK均值聚类在无监督的情况下选择图像特征的算法-K-meansK means clustering in the case of unsupervised image feature selection algorithm
EM-algorithm
- EM算法，是一种无监督的聚类算法，可以实现对数据的处理，对不同数据进行聚类，生成类内相似度最大-EM algorithm is an unsupervised clustering algorithm, the data processing can be achieved on different data clustering, to generate the maximum within-class similarity
GA1E1
- 用Ｋ均值和遗传算法实现了半监督聚类算法，这是个一个已经发表的论文的源程序-Using K-means and genetic algorithm to achieve a semi-supervised clustering algorithm, this is a paper published source
192010k-average
- kmeans均值聚类算法:一种改进的基于半监督聚类的入侵检测算法ASCID（Active-learning Semi-supervised Clustering Intrusion Detection），-kmeans clustering algorithm Algorithm was simulated by KDD 99 datasets， which the experimental results demonstrate that ASCID algorithm can impro
work_for_pattern_recognition
- 通过设计线性分类器；最小风险贝叶斯分类器；监督学习法分层聚类分析；K－L变换提取有效特征，设计支持向量机对给定样本进行有效分类并分析结果。-By designing a linear classifier minimum risk Bayes classifier supervised learning method hierarchical cluster analysis K-L transform to extract efficient features, designed to
ZPclustering
- 用于图像分割的自调整普聚类算法，可实现大多数图像的无监督分割-Self-tunning spectral clustering for image segmentation
lowdensityseparate
- 半监督LDS算法,是由Olivier Chapelle提出来的，他将半监督的流行假设和聚类假设结合起来。 -LDS for Semi-supervised learning，which is proposed by Olivier Chapelle,it combinations the monifold assumption and cluster assumption.
kmeans
- 基于k均值的无监督聚类算法，输出有各个样本的类别标签，目标函数在每次迭代后的值，聚类中心以及聚类区间。内有测试数据，点击 test.m 可以完美运行。(The unsupervised clustering algorithm based on K means outputs the class labels of each sample, the value of the target function after each iteration, the clustering center a
直觉模糊C均值聚类
- 对所获取的数据进行无监督的直觉模糊C均值聚类(intuitionistic fuzzy C-means clustering)
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.)
som
- 随机产生5类二维坐标系中的数，使用SOM网络进行无监督聚类，将产生的随机数自动聚成五类，并将结果用图像直接显示出来，生成训练好的网络权值(Five kinds of random numbers in two-dimensional coordinate system are generated randomly, and unsupervised clustering is carried out using SOM network. The random numbers generated
主动半监督K_means聚类算法研究及应用_吕峰.caj
- 基于师生模型实现半监督学习，百万级数据级(Semi supervised learning based on teacher-student model, million data level)
kmeans聚类算法
- kmeans聚类分析，无监督学习实现Matlab代码(Kmeans clustering analysis, unsupervised learning implementation of MATLAB code)