Survey of clustering algorithms

• Published in: IEEE transactions on neural networks Vol. 16; no. 3; pp. 645 - 678
• Main Authors: Rui Xu, Wunsch, D.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.05.2005; Institute of Electrical and Electronics Engineers
• Subjects: Adaptive resonance theory (ART); Algorithms; Application software; Applied sciences; Artificial intelligence; Bioinformatics; cluster validation; clustering; clustering algorithm; Clustering algorithms; Computer science; Computer science; control theory; systems; Computer Simulation; Connectionism. Neural networks; Data analysis; Exact sciences and technology; Humans; Machine learning; Machine learning algorithms; Models, Statistical; neural networks; Neural Networks (Computer); Numerical Analysis, Computer-Assisted; Pattern Recognition, Automated - methods; proximity; self-organizing feature map (SOFM); Signal Processing, Computer-Assisted; Statistics; Stochastic Processes; Traveling salesman problems; Validation; Data analysis; Proximity; Adaptive resonance theory; clustering algorithm; cluster validation; neural networks; Cluster; Neural network; self-organizing feature map (SOFM); Self-organising feature maps; Adaptive resonance theory (ART); clustering
• ISSN: 1045-9227
• DOI: 10.1109/TNN.2005.845141

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.