An efficient prototype merging strategy for the condensed 1-NN rule through class-conditional hierarchical clustering
An efficient prototype merging strategy for the condensed 1-NN rule through class-conditional hierarchical clustering
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DOI：
10.1016/s0031-3203(01)00208-4
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发表时间：
2002-12
期刊：
影响因子：
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通讯作者：
R. A. Mollineda;F. Ferri;E. Vidal
中科院分区：
文献类型：
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作者：
R. A. Mollineda;F. Ferri;E. Vidal
提出了一种基于层次聚类的广义原型分类方案。其基本思想是通过合并两个同类最近的聚类来获得一个浓缩的1-NN分类规则，前提是聚类代表集正确地分类所有原始点。除了质量所获得的集和它的灵活性，这来自于一个事实，即不同的集群间的措施和标准可以使用，该计划包括一个非常有效的四阶段的程序，方便地利用几何集群属性，以决定每个可能的合并。实验结果表明，该算法的优点，同时考虑到压缩集的原型，相应的压缩1-NN分类规则的准确性和计算时间的大小。
A generalized prototype-based classification scheme founded on hierarchical clustering is proposed. The basic idea is to obtain a condensed 1-NN classification rule by merging the two same-class nearest clusters, provided that the set of cluster representatives correctly classifies all the original points. Apart from the quality of the obtained sets and its flexibility which comes from the fact that different intercluster measures and criteria can be used, the proposed scheme includes a very efficient four-stage procedure which conveniently exploits geometric cluster properties to decide about each possible merge. Empirical results demonstrate the merits of the proposed algorithm taking into account the size of the condensed sets of prototypes, the accuracy of the corresponding condensed 1-NN classification rule and the computing time.