Discovering Complex Regularities: from Tree to Semi-Lattice Classifications

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 optimize classification by adding, moving or delete a neuron in order to change the number of classes. The tool is able to automatically suggest a strategy to optimize the number of classes optimization, but also support both tree classifications and semi-lattice organizations of the classes to give to the users the possibility of passing from one class to the ones with which it has some aspects in common. Examples of using tree and semi-lattice classifications are given to illustrate advantages and problems. The tool is applied 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 the 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. Possible interrelationships between the classes and their meaning are also discussed.

Authors:

• A. Faro
• D. Giordano
• F. Maiorana

Keywords:

• Unsupervised classification
• Kohonen networks
• macroeconomics
• Visual data mining
• Cluster interpretation.

References:

[1] D.Giordano and F.Maiorana, "A visual tool for mining macroeconomics
data". In A. Data mining V Zanasi, N.F.F. Ebecken, & C. Brebbia
(eds.), WIT Press, 2004.
[2] T. Kohonen, Self-Organizing Maps. Springer-Verlag, 2001.
[3] A.K.Jain, M.N.Murty, P.J.Flynn, "Data clustering: a review", ACM
Computing Surveys, Sept. 1999.
[4] S.Hautaniemi, O.Yli-HAria, J.Astola, et alii, "Analysis and visualization
of gene expression microarray data in human cancer using selforganizing
maps", Machine Learning 52, 45-66 2003
[5] M.Dittenbach, A.Rauber, D.Merkl, "Uncovering hierarchical structure in
data using the growing hierarchical self-organizing map",
Neurocomputing 48, 199-216, 2002
[6] A.J.Felders, "Data mining in economic science", http://www.cs.uu.nl/
people/ad/dmecon.pdf
[7] M.Lux, , "Visualization of financial data", Proc. Workshop on New
Paradigm in Information Visualization 1997
[8] 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
[9] C Alexander., Notes on the synthesis of form. Harvard Univ. Press 1971
[10] A.Faro and D.Giordano, "Concept formation from design cases: why
reusing experience and why not", Knowledge Based Systems vol.11 N.7-
8, 1998