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Neural Networks for Data Mining: Constrains and Open Problems
 

Summary: Neural Networks for Data Mining: Constrains
and Open Problems
Razvan Andonie and Boris Kovalerchuk
Computer Science Department
Central Washington University, Ellensburg, USA
Abstract. When we talk about using neural networks for data mining
we have in mind the original data mining scope and challenge. How
did neural networks meet this challenge? Can we run neural networks
on a dataset with gigabytes of data and millions of records? Can we
provide explanations of discovered patterns? How useful that patterns
are? How to distinguish useful, interesting patterns automatically? We
aim to summarize here the state-of-the-art of the principles beyond using
neural models in data mining.
1 What is special in data mining applications?
Data mining (DM) is the nontrivial extraction of implicit, previously unknown,
interesting, and potentially useful information (usually in the form of knowl-
edge patterns or models) from data. Historically data mining has grown from
large business database applications, such as finding patterns in customer pur-
chasing activities from transactions databases. Original DM problems were to
adjust known methods such as decision trees and neural networks (NN) to large

  

Source: Andonie, Razvan - Department of Computer Science, Central Washington University

 

Collections: Computer Technologies and Information Sciences