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JAVA 本程序所实现的功能为对数据进行无监督的学习，即聚类算法-JAVA the procedures for the functions of data unsupervised learning, clustering algorithm
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中心点漂移是一种非监督聚类算法（与k-means算法相似，但应用范围更广些),可用于图像分割，基于Matlab实现的源码。
MedoidShift is a unsupervised clustering algorithm(similar to k-means algorithm, but can be used in border application fields), can be used for image segmentation. Included is the Matlab
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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.
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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
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文本聚类，VC编程实现，作为一种无监督的机器学习方法，聚类由于不需要训练过程，以及不需要预先对文档手工标注类别，因此具有一定的灵活性和较高的自动化处理能力-Text Clustering, VC programming, as an unsupervised machine learning method, clustering by eliminating the need for the training process, and do not need to manually pre-ma
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isodata是个重要的非监督聚类算法，本文件提供了isodata的c++描述-isodata is an important unsupervised clustering algorithm, this paper provides a isodata of c++ descr iption
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基于无监督学习的谱聚类算法的文本的聚类分类。-Unsupervised Learning Based on spectral clustering algorithm for text clustering classification.
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In statistics, a mixture model is a probabilistic model for density estimation using a mixture distribution. A mixture model can be regarded as a type of unsupervised learning or clustering. Mixture models should not be confused with models for compo
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无监督学习与聚类课件，介绍了无监督学习算法-unsupervised clustering
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非监督聚类的方法，该论文描述如何使用分形维数进行聚类判定准则，从而达到理想的聚类效果，经典论文-Unsupervised clustering method, the paper describes how to use the fractal dimension of the clustering criteria, in order to achieve the desired clustering effect, the classic paper
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kmeans聚类，用于时间序列无监督聚类-kmeans clustering for unsupervised clustering time series
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kmeans算法，最基本的聚类算法，用于完成无指导的聚类问题，空间时间复杂性不高。-kmeans algorithm, the basic clustering algorithm, for the completion of unsupervised clustering problem, space-time complexity is not high.
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image classification algorithm : k-means clustering implementation is provided herewith, which is an unsupervised clustering method
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this for unsupervised clustering again
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Unsupervised Learning - Clustering
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自组织特征映射网络实现无监督聚类，调用工具箱，非手算-Self organizing feature map network to realize unsupervised clustering, call tool box, non hand count
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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
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基于fuzzy c-means（FCM）的无监督模糊聚类算法，输出值有：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data.mat，点击 test.m 可以完美运行。(The unsupervised fuzzy clustering algorithm based on fuzzy c-means (FCM) outputs the class labels of each sample, the value of the target function
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UNSUPERVISED LOCATION-BASED SEGMENTATION OF MULTI-PARTY SPEECH的配套源码,G. Lathoud, I.A. McCowan and J.M. Odobez
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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
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