CSOM for Mixed Data Types

Authors:
Fedja Hadzic;Tharam S. Dillon
Affiliations:
Faculty of Information Technology, University of Technology Sydney, Australia;Faculty of Information Technology, University of Technology Sydney, Australia
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
Year:
2007

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Abstract

In our previous work we presented a variation of Self-Organizing Map (SOM), CSOM that applies a different learning mechanism useful for situations where the aim is to extract rules from a data set characterized by continuous input features. The main change is that the weights on the network links are replaced by ranges which allows for a direct extraction of the underlying rule. In this paper we extend our work by allowing the CSOM to handle mixed data types and continuous class attributes. These extensions called for an appropriate adjustment in the network pruning method that uses the Symmetrical Tau (茂戮驴) criterion for measuring the predictive capability of cluster attributes. Publicly available real world data sets were used for evaluating the proposed method and the results demonstrate the effectiveness of the method as a whole for extracting optimal rules from a trained SOM.