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经典的k均值聚类算法，用于无监督学习和数据分类，广泛应用于数据处理和模式识别领域-Classical k-means clustering algorithm for unsupervised learning and data classification, widely used in data processing and pattern recognition
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机器学习算法，无监督学习，利用k均值聚类算法对未标注数据分组-Machine learning algorithms, unsupervised learning, the use of k-means clustering algorithm for unlabeled data packets
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This work introduces two variants of unsupervised color segmentation methods. The underlying idea is to segment the input image several times, each time focussing on a different salient part of the image and to subsequently merge all obtained result
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An important aspect of an ANN model is whether it needs
guidance in learning or not. Based on the way they learn, all
artificial neural networks can be divided into two learning
categories - supervised and unsupervised.
• In supervised l
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机器学习的范畴，包括SVMs (based on libsvm), k-NN, random forests, decision trees。可以对任意的数据操作-Its focus is on supervised classification with several classifiers available: SVMs (based on libsvm), k-NN, random forests, decision trees. It also performs feature sel
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One challenging problem for search-based face annotation scheme is how to effectively perform annotation by exploiting the list of most similar facial images and their weak labels that are often noisy and incomplete. To tackle this problem, we propos
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Clustering and Projected Clustering with Adaptive Neighbors
-We proposed a CAN clustering algorithm with adaptive neighbors, the learned similarity matrix can be directly used for clustering, without having to perform K-means or other discretizatio
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k-means is one of the simplest unsupervised learning algorithms that solve the well known clustering problem. The procedure follows a simple and easy way to classify a given data set through a certain number of clusters (assume k clusters) fixed apri
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聚类算法可以理解为无监督的分类方法，即样本集预先不知所属类别或标签。-Clustering algorithms can be understood as unsupervised classification method, namely the sample set in advance know that category or tag.
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k-means is a algorithm for make unsupervised learning, which use a vector, value k for create the clustering
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用于数据聚类，无监督学习，可以直接使用，望大家指导批评(For data clustering, unsupervised learning)
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自组织竞争神经网络，可以对数据进行无监督学习聚类，本质上是一种只有输入层--隐藏层的神经网络(The self-organized competitive neural network can carry out unsupervised learning clustering of data, which is essentially a neural network with only the input layer hidden layer.)
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自组织神经网络概念和原理，并重点介绍一下自组织特征映射SOM网络。SOM和现在流行的ANN（MLP）模型在结构上类似，都由非常简单的神经元结构组成，但是SOM是一类“无监督学习”模型，一般的用法是将高维的input数据在低维的空间表示[1]，因此SOM天然是一种降维方法。除了降维，SOM还可以用于数据可视化，以及聚类等应用中。(The concept and principle of self-organizing neural network are introduced, and the s
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文本聚类(Text clustering)文档聚类主要是依据著名的聚类假设:同类的文档相似度较大，而不同类的文档相似度较小。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，已经成为对文本信息进行有效地组织、摘要和导航的重要手段，为越来越多的研究人员所关注。(Text clustering document clustering is based on the well-known clustering assum
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kmeans聚类分析，无监督学习实现Matlab代码(Kmeans clustering analysis, unsupervised learning implementation of MATLAB code)
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