Self-organizing systems for knowledge discovery in large databases

William H. Hsu, Loretta S. Auvil, William M. Pottenger, David Tcheng, Michael Welge

Research output: Chapter in Book/Report/Conference proceedingConference contribution


We present a framework in which self-organizing systems can be used to perform change of representation on knowledge discovery problems, to learn from very large databases. Clustering using self-organizing maps is applied to produce multiple, intermediate training targets that are used to define a new supervised learning and mixture estimation problem. The input data is partitioned using a state space search over subdivisions of attributes, to which self-organizing maps are applied to the input data as restricted to a subset of input attributes. This approach yields the variance-reducing benefits of techniques such as stacked generalization, but uses self-organizing systems to discover factorial (modular) structure among abstract learning targets. This research demonstrates the feasibility of applying such structure in very large databases to build a mixture of ANNs for data mining and KDD. Areas of applications include multi-attribute risk assessment using insurance policy data, text document categorization, and anomaly detection.

Original languageEnglish (US)
Title of host publicationProceedings of the International Joint Conference on Neural Networks
Number of pages6
StatePublished - 1999
EventInternational Joint Conference on Neural Networks (IJCNN'99) - Washington, DC, USA
Duration: Jul 10 1999Jul 16 1999


OtherInternational Joint Conference on Neural Networks (IJCNN'99)
CityWashington, DC, USA

ASJC Scopus subject areas

  • Software


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