Open AccessApplication of Neural Networks to Data Mining
Author(s) -
Zlatinka Kovacheva
Publication year - 2007
Publication title -
maǧallaẗ ǧāmiʿaẗ al-sulṭān qābūs li-l-ʿulūm/sultan qaboos university journal for science
Language(s) - English
Resource type - Journals
eISSN - 2414-536X
pISSN - 2308-3921
DOI - 10.24200/squjs.vol12iss2pp121-141
Subject(s) - artificial neural network , computer science , process (computing) , data mining , machine learning , artificial intelligence , knowledge extraction , software , data science , operating system , programming language
Data Mining accomplishes nontrivial extraction of implicit, previously unknown, and potentially useful information of data. The aim of data mining is to discover knowledge out of data and present it in a form that is easily comprehensible to humans. Neural Networks are analytic techniques capable of predicting new observations from other observations after executing a process of so-called learning from existing data. Neural Network techniques can also be used as a component of analyses designed to build explanatory models. Now there is neural network software that uses sophisticated algorithms directly contributing to the model building process. The latest developments in research on neural networks bring them much closer to the ideal of data mining: knowledge out of data in understandable terms. The main goal of the review is to compare neural networks with other techniques for data mining and to overview some examples of application of neural networks to data mining processes in practice.