A Three-layered Self-Organizing Map Neural Network for Clustering Analysis

In the commercial world today, holding the effective information through information technology (IT) and the internet is a very important indicator of whether an enterprise has competitive advantage in business. Clustering analysis, a technique for data mining or data analysis in databases, has been widely applied in various areas. Its purpose is to segment the individuals in the same population according to their characteristics. In this research, an enhanced three-layered self-organizing map neural network, called 3LSOM, is developed to overcome the drawback of the conventional two-layered SOM through sight-inspection after the mapping process. To further verify its feasibility, the proposed model is applied to two common problems: the identification of four given groups of work-part images and the clustering of a machine/part incidence matrix. The experimental results prove that the data that belong to the same group can be mapped to the same neuron on the output layer of the 3LSOM. Its performance in clustering accuracy is good and is also comparable with that of the FSOM, FCM and k-Means