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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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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
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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
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enhancing semi-supervised clustering:a feature projection prespective算法实现-the implementation of the alogrithm described in the paper--- enhancing semi-supervised clustering:a feature projection prespective
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一本将基于近邻传播算法的半监督聚类的算方法书.对于聚类研究的很有帮助-Abstract: 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 is an efficient a
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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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实现半监督聚类，针对weka框架进行扩展。-It realize semi-supervised clustering method. And it is extension of weka.
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义了一个欧氏距离和监督信息相混合的新的最近邻计算函数，从而将K一均值算法很好地应用于半
监督聚类问题。针对K一均值算法初始质心敏感的缺陷，用粒子群算法的搜索空间模拟聚类的欧氏空间，迭代搜
索找到较优的聚类质心，同时提出动态管理种群的策略以提高粒子群算法搜索效率。算法在UCI的多个数据集
上测试都得到了较好的聚类准确率。-Righteousness of a Euclidean distance and supervision of a mixture of new nearest n
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利用谱聚类方法在特
征向量空间中对原始样本数据进行重新表述使得在新表述中同一聚类中的样本能够更好地积聚在一起构建聚类核函数 并进而构造聚类核半监督支持向量机 使样本更好地满足半监督学习必须遵循的聚类假设 -Restated in the new formulation in the same cluster sample be better able to accumulate together to build the clustering of nuclear function and
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一个半监督数据流集合分类器程序，其中每个单分类器采用k均值聚类算法。-A semi-supervised data flow classifier using k-means clustering algorithm.
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Semi-supervised Kernel Mean Shift Clustering
Authors: Saket Anand, Sushil Mittal, Oncel Tuzel and Peter Meer-Semi-supervised Kernel Mean Shift Clustering Authors: Saket Anand, Sushil Mittal, Oncel Tuzel and Peter MeerSemi-supervised Kernel Mean Shi
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半监督聚类是利用少量的标记数据提高聚类算法的性能，文中综述了半监督聚类算法的若干进展-Semi supervised clustering is a method to improve the performance of clustering algorithm by using a small amount of labeled data,Some advances about semi supervised clustering algorithms are reviewed in thi
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This file belongs to semi supervised clustering
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