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

Abstract

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
PublisherIEEE
Pages2480-2485
Number of pages6
Volume4
StatePublished - 1999
EventInternational Joint Conference on Neural Networks (IJCNN'99) - Washington, DC, USA
Duration: Jul 10 1999Jul 16 1999

Other

OtherInternational Joint Conference on Neural Networks (IJCNN'99)
CityWashington, DC, USA
Period7/10/997/16/99

ASJC Scopus subject areas

  • Software

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