聚类分析:现代统计回顾

IF 4.4 2区 数学 Q1 STATISTICS & PROBABILITY
Adam Jaeger, David Banks
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引用次数: 8

摘要

聚类分析是一个庞大而庞大的领域。这份检讨文件不可能全面调查全港。相反,它关注的是分层聚集聚类、k均值聚类、混合模型,以及任何聚类分析从业者都应该意识到的几个相关主题。即便如此,这次审查也无法公正地对待所选的主题。有很多文学作品,而且往往都是即兴创作的。这通常是聚类分析的本质——每个应用程序都需要定制的分析。尽管如此,聚类已被证明在生物学、广告、推荐系统和基因组学中作为一种探索性数据分析工具是非常有用的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Cluster analysis: A modern statistical review
Cluster analysis is a big, sprawling field. This review paper cannot hope to fully survey the territory. Instead, it focuses on hierarchical agglomerative clustering, k‐means clustering, mixture models, and then several related topics of which any cluster analysis practitioner should be aware. Even then, this review cannot do justice to the chosen topics. There is a lot of literature, and often it is somewhat ad hoc. That is generally the nature of cluster analysis—each application requires a bespoke analysis. Nonetheless, clustering has proven itself to be incredibly useful as an exploratory data analysis tool in biology, advertising, recommender systems, and genomics.
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来源期刊
CiteScore
6.20
自引率
0.00%
发文量
31
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