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6 April 2000 Numerical-linguistic knowledge discovery using granular neural networks
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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.
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Yanqing Zhang "Numerical-linguistic knowledge discovery using granular neural networks", Proc. SPIE 4057, Data Mining and Knowledge Discovery: Theory, Tools, and Technology II, (6 April 2000); https://doi.org/10.1117/12.381721
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