An extended self-organizing map network for market segmentation: a telecommunication example
Decision Support Systems
Denormalization strategies for data retrieval from data warehouses
Decision Support Systems
A comparative analysis of an extended SOM network and K-means analysis
International Journal of Knowledge-based and Intelligent Engineering Systems
Computational Statistics & Data Analysis
Suitability of self-organising maps for analysing a macro-environment an empirical field survey
International Journal of Business Information Systems
Selecting the right MBA schools - An application of self-organizing map networks
Expert Systems with Applications: An International Journal
Profiling blood donors in Egypt: A neural network analysis
Expert Systems with Applications: An International Journal
Recognition of Western style musical genres using machine learning techniques
Expert Systems with Applications: An International Journal
A two-stage clustering approach for multi-region segmentation
Expert Systems with Applications: An International Journal
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Kohonen's self-organizing map (SOM) network is one of the most important network architectures developed during the 1980s. The main function of SOM networks is to map the input data from ann-dimensional space to a lower dimensional (usually one- or two-dimensional) plot while maintaining the original topological relations. Therefore, it can be viewed as an analog of factor analysis. In this research, we evaluate the feasibility of using SOM networks as a robust alternative to factor analysis and clustering for data mining applications. Specifically, we compare SOM network solutions to factor analytic and K-Means clustering solutions on simulated data sets with known underlying factor and cluster structures.The comparisons indicate that the SOM networks provide solutions superior to unrotated factor solutions in general and provide more accurate recovery of underlying cluster structures when the input data are skewed. Our findings suggest that SOM networks can provide robust alternatives to traditional factor analysis and clustering techniques in data mining applications.