.png){kind=icon}
返回
An evolutionary approach to multiobjective clustering
.png){kind=icon}
PRE
AI
DOI：10.1109/TEVC.2006.877146.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
Keyword:
clustering
determination of the number of clusters
evolutionary clustering
model selection
multiobjective clustering
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
12
论文数:
1.9K
被引数:
2.4W
机构
暂无机构信息
引用论文
分享收藏
Computational cluster validation in post-genomic data analysis后基因组数据分析中的计算聚类验证.png)
BIOINFORMATICS
OAAI
BIOINFORMATICSIF5.4
OAAI分享收藏
查看更多内容