Interpretation of self-organizing maps with fuzzy rules

M. Drobics, W. Winiwater, U. Bodenhofer

Research output: Chapter in Book/Report/Conference proceedingsConference contributionpeer-review

10 Citations (Scopus)

Abstract

Exploration of large and high-dimensional data sets is one of the main problems in data analysis. Self-organizing maps (SOMs) can be used to map large data sets to a simpler; usually two-dimensional topological structure. This mapping is able to illustrate dependencies in the data in a very intuitive manner and allows fast location of clusters. However because of the black-box design of neural networks, it is difficult to get qualitative descriptions of the data. In our approach, we identify regions of interest in SOMs by using unsupervised clustering methods. Then we apply inductive learning methods to find fuzzy descriptions of these clusters. Through the combination of these methods, it is possible to use supervised machine learning methods to find simple and accurate linguistic descriptions of previously unknown clusters in the data.

Original languageEnglish
Title of host publicationProceedings - 12th IEEE Internationals Conference on Tools with Artificial Intelligence, ICTAI 2000
PublisherIEEE Computer Society
Pages304-311
Number of pages8
ISBN (Electronic)0769509096
DOIs
Publication statusPublished - 2000
Event12th IEEE Internationals Conference on Tools with Artificial Intelligence, ICTAI 2000 - Vancouver, Canada
Duration: 13 Nov 200015 Nov 2000

Publication series

NameProceedings - International Conference on Tools with Artificial Intelligence, ICTAI
Volume2000-January
ISSN (Print)1082-3409

Conference

Conference12th IEEE Internationals Conference on Tools with Artificial Intelligence, ICTAI 2000
CountryCanada
CityVancouver
Period13.11.200015.11.2000

Keywords

  • Clustering methods
  • Data analysis
  • Data mining
  • Databases
  • Fuzzy sets
  • Natural languages
  • Neural networks
  • Neurons
  • Production
  • Self organizing feature maps

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