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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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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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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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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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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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