An evolutionary approach to multiobjective clustering
An evolutionary approach to multiobjective clustering
复制标题
DOI：
10.1109/tevc.2006.877146
复制
发表时间：
2007-02-01
影响因子：
14.3
通讯作者：
Knowles, Joshua
中科院分区：
文献类型：
--
作者：
Handl, Julia;Knowles, Joshua
多目标优化的框架是用来解决无监督学习问题，数据聚类，制定首次提出的统计文献。多目标制定的概念优势进行了讨论，并开发了一个进化的方法来解决这个问题。由此产生的算法，多目标聚类与自动k-测定，比较了一些行之有效的单目标聚类算法，现代集成技术，和两种方法的模型选择。实验表明，多目标聚类的概念优势转化为实际和可扩展的性能优势。
The framework of multiobjective optimization is used to tackle the unsupervised learning problem, data clustering, following a formulation first proposed in the statistics literature. The conceptual advantages of the multiobjective formulation are discussed and an evolutionary approach to the problem is developed. The resulting algorithm, multiobjective clustering with automatic k-determination, is compared with a number of well-established single-objective clustering algorithms, a modern ensemble technique, and two methods of model selection. The experiments demonstrate that the conceptual advantages of multiobjective clustering translate into practical and scalable performance benefits.