Discovering Complex Regularities by Adaptive Self Organizing Classification
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.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1057579Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1227
 D. Giordano, F. Maiorana. A visual tool for mining macroeconomics data. In A. Zanasi, N.F.F. Ebecken, & C. Brebbia (eds.): Data mining V. WIT Press, 2004.
 Kohonen, T. Self-Organizing Maps. Springer-Verlag, 2001.
 Jain, AK, Murty, M.N. Flynn, P.J. Data clustering: a review. ACM Computing Surveys, Sept. 1999.
 Hautaniemi, S. Yli-HAria, O. Astola, J. et al. (2003). Analysis and visualization of gene expression microarray data in human cancer using self-organizing maps. Machine Learning 52, 45-66.
 Dittenbach M, Rauber, A. Merkl, D. (2002). Uncovering hierarchical structure in data using the growing hierarchical self-organizing map. Neurocomputing 48 (2002) 199-216.
 Felders, A. J. Data mining in economic science
[Online]. Available at : http://www.cs.uu.nl/people/ad/dmecon.pdf.
 Lux, M. "Visualization of financial data" in Proc. Workshop on New Paradigm in Information Visualization (1997)
 L.Bordoni, D. Giordano, S.Spadaro. Il data mining: un-applicazione agli studi macroeconomici. Atti del convegno AICA 2002 (Associazione Italiana Calcolo Automatico), pp. 557 - 61, 2002.