Exemplar-Based Clustering via Simulated Annealing
Exemplar-Based Clustering via Simulated Annealing
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DOI：
10.1007/s11336-009-9115-2
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发表时间：
2009-09-01
期刊：
影响因子：
3
通讯作者：
Koehn, Hans-Friedrich
中科院分区：
文献类型：
--
作者：
Brusco, Michael J.;Koehn, Hans-Friedrich
一些作者吹捧p-中位数模型是簇内平方和的合理替代方案（即，K-means）分割。所谓的优势的p-中位数模型包括提供的“样本”作为聚类中心，鲁棒性相对于离群值，并容纳各种各样的相似性数据。我们开发了一个新的模拟退火启发式的p-中位数问题，并完成了彻底的调查，其计算性能。从我们的实验中的显着的发现是，我们的新方法大大优于以前的模拟退火的实现，并与最有效的元分析的p-中位数问题的竞争力。
Several authors have touted the p-median model as a plausible alternative to within-cluster sums of squares (i.e., K-means) partitioning. Purported advantages of the p-median model include the provision of "exemplars" as cluster centers, robustness with respect to outliers, and the accommodation of a diverse range of similarity data. We developed a new simulated annealing heuristic for the p-median problem and completed a thorough investigation of its computational performance. The salient findings from our experiments are that our new method substantially outperforms a previous implementation of simulated annealing and is competitive with the most effective metaheuristics for the p-median problem.