一种可优化相异性度量的Affinity Propagation聚类算法
n Affinity Propagation Clustering Algorithm Embedded in Optimizing Dissimilarity Measure
为获得更贴近于混合属性数据点集空间的相异性度量，从而探测出数据点集的更有意义的聚类分布，提出了一种可优化相异性度量的Affinity Propagation聚类算法(算法1)。接着对该聚类算法进行了必要的讨论，给出其时间复杂度及收敛性分析。通过German数据集的几种聚类算法的对照实验结果及评估相异性度量的比较实验结果，验证了该聚类算法有时能取得更好的聚类精度，从而说明该加权聚类算法具有一定的有效性。最后给出了几点研究展望，为下一步的研究指明了方向。
It gives an Affinity Propagation Clustering algorithm embedded in Optimizing Dissimilarity measure (APCOD), described by algorithm 1, in order to find more meaningful clustering distributions by searching a better dissimilarity measure in hybrid attributes data space. After some necessary discuss, it lists its time complexity and astringency analysis. The APCOD can some time get a better clustering quality validated by experiments using German data sets. At last, it indicates several valuable research expectations.
陈新泉
计算技术、计算机技术
相异性度量ffinity Propagation有序属性无序属性混合属性
issimilarity Measureffinity PropagationOrdered AttributesSorted AttributesHybrid Attributes
陈新泉.一种可优化相异性度量的Affinity Propagation聚类算法[EB/OL].(2009-06-30)[2025-05-25].http://www.paper.edu.cn/releasepaper/content/200906-811.点此复制
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