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Intelligent Engineering Systems through Artificial Neural Networks Volume 18

Cihan H. Dagli
Cihan H. Dagli
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ASME Press
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A common challenge today, arising especially in the field of text and web mining, is the proper visualization of very large datasets to explore their structure and gain information that otherwise would remain concealed, buried due to the sheer amount of data. These large non-Euclidean datasets cannot be held at once within random-access memory during computation, so fast batch variants of common mapping methods like Self-Organizing Maps (SOM) or Neural Gas (NG) cannot be applied. In this work, we present fast approximate (semi-)supervised versions of SOM and NG for non-Euclidean data that are able to handle large text datasets by...

I. Introduction
II. Topographic Maps
III. Relational Data
IV. Patch Relational Methods
V. Experiments
VI. Conclusions
VII. Appendix
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