Аннотации:
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.
keywords: {data analysis;pattern classification;pattern clustering;benchmark data sets;bioinformatics;cluster analysis;data analysis;traveling salesman problem;Application software;Bioinformatics;Clustering algorithms;Computer science;Data analysis;Humans;Machine learning;Machine learning algorithms;Statistics;Traveling salesman problems;Adaptive resonance theory (ART);cluster validation;clustering;clustering algorithm;neural networks;proximity;self-organizing feature map (SOFM);Algorithms;Computer Simulation;Models, Statistical;Neural Networks (Computer);Numerical Analysis, Computer-Assisted;Pattern Recognition, Automated;Signal Processing, Computer-Assisted;Stochastic Processes},
