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Proceedings Paper

Numerical-linguistic knowledge discovery using granular neural networks
Author(s): Yanqing Zhang
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Paper Abstract

In this paper, a granular-neural-network-based Knowledge Discovery and Data Mining (KDDM) method based on granular computing, neural computing, fuzzy computing, linguistic computing and pattern recognition is presented. The major issues include (1) how to use neural networks to discover granular knowledge from numerical-linguistic databases, and (2) how to use discovered granular knowledge to predict missing data. A Granular Neural Network (GNN) is designed to deal with numerical-linguistic data fusion and granular knowledge discovery in numerical-linguistic databases. From a data granulation point of view, the GNN can process granular data in a database. From a data fusion point of view, the GNN makes decisions based on different kinds of granular data. From a KDDM point of view, the GNN is able to learn internal granular relations between numerical-linguistic inputs and outputs, and predict new relations in a database.

Paper Details

Date Published: 6 April 2000
PDF: 8 pages
Proc. SPIE 4057, Data Mining and Knowledge Discovery: Theory, Tools, and Technology II, (6 April 2000); doi: 10.1117/12.381721
Show Author Affiliations
Yanqing Zhang, Georgia State Univ. (United States)

Published in SPIE Proceedings Vol. 4057:
Data Mining and Knowledge Discovery: Theory, Tools, and Technology II
Belur V. Dasarathy, Editor(s)

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