TY - JFULL AU - A. Faro and D. Giordano and F. Maiorana PY - 2007/5/ TI - Discovering Complex Regularities by Adaptive Self Organizing Classification T2 - International Journal of Computer and Information Engineering SP - 994 EP - 998 VL - 1 SN - 1307-6892 UR - https://publications.waset.org/pdf/2623 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 4, 2007 N2 - Data mining uses a variety of techniques each of which is useful for some particular task. It is important to have a deep understanding of each technique and be able to perform sophisticated analysis. In this article we describe a tool built to simulate a variation of the Kohonen network to perform unsupervised clustering and support the entire data mining process up to results visualization. A graphical representation helps the user to find out a strategy to optmize classification by adding, moving or delete a neuron in order to change the number of classes. The tool is also able to automatically suggest a strategy for number of classes optimization.The tool is used to classify macroeconomic data that report the most developed countries? import and export. It is possible to classify the countries based on their economic behaviour and use an ad hoc tool to characterize the commercial behaviour of a country in a selected class from the analysis of positive and negative features that contribute to classes formation. ER -