A comparison of SOM neural network and hierarchical clustering methods
Document Type
Article
Date of Original Version
9-6-1996
Abstract
Cluster analysis, the determination of natural subgroups in a data set, is an important statistical methodology that is used in many contexts. A major problem with hierarchical clustering methods used today is the tendency for classification errors to occur when the empirical data departs from the ideal conditions of compact isolated clusters. Many empirical data sets have structural imperfections that confound the identification of clusters. We use a Self Organizing Map (SOM) neural network clustering methodology and demonstrate that it is superior to the hierarchical clustering methods. The performance of the neural network and seven hierarchical clustering methods is tested on 252 data sets with various levels of imperfections that include data dispersion, outliers, irrelevant variables, and nonuniform cluster densities. The superior accuracy and robustness of the neural network can improve the effectiveness of decisions and research based on clustering messy empirical data.
Publication Title, e.g., Journal
European Journal of Operational Research
Volume
93
Issue
2
Citation/Publisher Attribution
Mangiameli, Paul, Shaw K. Chen, and David West. "A comparison of SOM neural network and hierarchical clustering methods." European Journal of Operational Research 93, 2 (1996): 402-417. doi: 10.1016/0377-2217(96)00038-0.