聚类分析
计算机科学
旅行商问题
混乱
水准点(测量)
领域(数学)
多样性(控制论)
机器学习
数据挖掘
数据科学
人工智能
星团(航天器)
算法
数学
心理学
大地测量学
精神分析
纯数学
程序设计语言
地理
标识
DOI:10.1109/tnn.2005.845141
摘要
Data analysis plays an indispensable role for understanding various phenomena. Cluster analysis, primitive exploration with little or no prior knowledge, consists of research developed across a wide variety of communities. The diversity, on one hand, equips us with many tools. On the other hand, the profusion of options causes confusion. We survey clustering algorithms for data sets appearing in statistics, computer science, and machine learning, and illustrate their applications in some benchmark data sets, the traveling salesman problem, and bioinformatics, a new field attracting intensive efforts. Several tightly related topics, proximity measure, and cluster validation, are also discussed.
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