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Intelligent Engineering Systems through Artificial Neural Networks Volume 18

Editor
Cihan H. Dagli
Cihan H. Dagli
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ISBN-10:
0791802823
ISBN:
9780791802823
No. of Pages:
700
Publisher:
ASME Press
Publication date:
2008

In this paper, we propose a general framework referred to as interactive clustering and classification (ICC). It is designed to identify sub-groups of samples with different model structures. The framework features an interaction between classification and clustering process, allowing the clustering process to be partially driven by the classification process and is therefore, presumably, more informative. The method is tested rigorously on both synthetic datasets and real world problems. Experimental results demonstrate that ICC provided a good approximation of complex model structure by an aggregation of simple models while circumventing the issue of over-fitting.

Abstract
1 Introduction
2 Interactive Clustering and Classification
3. Preliminary Experimental Results
4. Conclusions
Reference
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