Communications of the ACM
Simplifying neural networks by soft weight-sharing
Neural Computation
Extracting Refined Rules from Knowledge-Based Neural Networks
Machine Learning
An architecture of fuzzy neural networks for linguistic processing
Fuzzy Sets and Systems
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Mathematical Methods for Neural Network Analysis and Design
Mathematical Methods for Neural Network Analysis and Design
Fuzzy Sets Engineering
A Strategy for Increasing the Efficiency of Rule Discovery in Data Mining
IDA '97 Proceedings of the Second International Symposium on Advances in Intelligent Data Analysis, Reasoning about Data
A Connectionist Approach to Extracting Knowledge from Databases
IDA '97 Proceedings of the Second International Symposium on Advances in Intelligent Data Analysis, Reasoning about Data
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This chapter elaborates on the connections and interdisciplinary links between knowledge discovery in databases (KDD) and neural networks and neurocomputing, in general. We identify a number of basic categories of synergistic links existing therein. We show that data mining can benefit from the learning abilities of neural networks. Similarly, there are ways in which data mining can augment the research agenda of neurocomputing by drawing attention to the issues of processing large data sets and identifying possible ways of learning enhancement through data granulation. The aspect of increased transparency of neural networks is another essential topic promoted by KDD.