Abstract
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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