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.
R. Xu and D. C. Wunsch, "Survey of Clustering Algorithms," IEEE Transactions on Neural Networks, Institute of Electrical and Electronics Engineers (IEEE), May 2005.
The definitive version is available at https://doi.org/10.1109/TNN.2005.845141
Electrical and Computer Engineering
Mary K. Finley Missouri Endowment
National Science Foundation (U.S.)
Keywords and Phrases
Adaptive Resonance Theory (ART); Cluster Validation; Clustering; Clustering Algorithm; Proximity; Self-Organizing Feature Map (SOFM); Neural networks (Computer science)
International Standard Serial Number (ISSN)
Article - Journal
© 2005 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
01 May 2005