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：在有限区域内分布的稀疏不均的、具有一定分布结构的海量数据集的高效、高精度聚
类问题是一个尚未完全圆满解决的难题。针对Affinity Propagation 聚类算法(AP)的不足之处，
提出了两个改进型的聚类算法-n limited areas of uneven distribution of sparse, has certain distribution structure of the mass datasets of high efficiency, high precis
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最新AP聚类算法以及演示程序，算法内容参照affinity appropagation in science。-AP latest clustering algorithm as well as the demo program, algorithm reference content affinity appropagation in science.
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AP是在数据点的相似度矩阵的基础上进行聚类.对于规模很大的数据集,AP算法是一种快速、有效的聚类方法,这是其他传统的聚类算法所不能及的,-A semi-supervised clustering method based on affinity propagation (AP) algorithm is proposed in this paper. AP takes as input measures of similarity between pairs of data points. AP
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MS WORD document describing & comparing k means clustering and affinity propagation
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一种新的聚类算法，被翻译为“吸引力传播聚类”，希望对研究模式识别的同学有帮助，谢谢啦-A new clustering algorithm is provided, which is called the Affinity Propagation Clustering. I hope it is helpful for people major in PR. Thank you for your attention.
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APCLUSTER Affinity Propagation Clustering (easy to usemodify, but no sparse capability)
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包好一个AP算法matlab程序，以及最早提出AP算法的经典文章及相应PPT-Affinity Propagation algorithm matlab
Clustering by Passing Messages Between Data Points.pdf
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Semi-supervised Affinity Propagation clustering.基于AP聚类的半监督学习算法。-The programs of semi-supervised AP are suitable for the person who has interests in studying or improving AP algorithm,
and then the semi-supervised AP may be an example for reference
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在有限区域内分布的稀疏不均的、具有一定分布结构的海量数据集的高效、高精度聚
类问题是一个尚未完全圆满解决的难题。针对Affinity Propagation 聚类算法(AP)的不足之处，
提出了两个改进型的聚类算法-In a limited area of uneven distribution of sparse, has certain distribution structure of the mass datasets of high efficiency, high preci
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07年发到Science上的关于聚类的文章《Clustering by passing messages between data points》的聚类算法--紧邻传播算法的源代码！-The source code of the clustering algorithm of <<Clustering by passing messages between data points>>
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Affinity propagation clustering matlab code.
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Affinity Propagation(AP)聚类方法的原始版本，并附加了在Science杂志上的原文献，对于研究AP算法的人会有很多参考价值。-The original version of the AP clustering method. Attached the original documents in the journal Science,it will have a lot of reference value for studying the AP algorithm.
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相似性传播聚类，不需要初始化聚类中心，聚类速度优于k-maans，k-centers等聚类算法-Affinity propagation clustering, do not need to initialize the cluster center, cluster velocity than k-maans, k-centers clustering algorithm, etc.
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Affinity propagation聚类算法的python编程文件！-Affinity propagation clustering algorithm python programming file!
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Affinity propagation （AP）聚类算法的JAVA程序！可参考再修改!-Affinity propagation (AP) clustering algorithm JAVA program! Refer again to modify!
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本程序为仿射传播聚类的算法，相比较于K均值聚类不需要确定聚类个数，且对初始聚类中心不敏感-This procedure for affinity propagation clustering algorithm, compared to K-means clustering is not required to determine the number of clusters, and is not sensitive to the initial cluster centers
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Affinity Propagation聚类算法源码（Matlab）。內附两个测试用例（ToyProblem.mat，FaceClusteringSimilarities.zip）-Affinity Propagation Clustering algorithm source code and two dataset for testing
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Color Image Segmentation using Self-Adaptive Watershed and Affinity Propagation Clustering
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Affinity Propagation (AP) 聚类是2007年在Science杂志上提出的一种新的聚类算法。它根据N个数据点之间的相似度进行聚类,这些相似度可以是对称的,即两个数据点互相之间的相似度一样(如欧氏距离) 也可以是不对称的,即两个数据点互相之间的相似度不等。-Affinity Propagation (AP) Clustering is a new clustering algorithm in 2007 in Science magazine raised. It clust
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Affinity propagation聚类算法PYTHON版本(Affinity propagation Clustering Algorithm)
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