Potential use of Artificial Neural Network in Data Mining

Abstract

With the enormous amount of data stored in files, databases, and other repositories, it is increasingly important, to develop powerful means for analysis and perhaps interpretation of such data and for the extraction of interesting knowledge that could help in decision-making. Data Mining, also popularly known as Knowledge Discovery in Databases (KDD), refers to the nontrivial extraction of implicit, previously unknown and potentially useful information from data in databases. Thus data mining is the process of automated extraction of hidden, predictive information from large databases. Data mining includes: extract, transform, and load transaction data onto the data warehouse system. Neural networks have been successfully applied in a wide range of supervised and unsupervised learning applications. Neural-network methods are not commonly used for data-mining tasks, because they may have complex structure, long training time, and uneasily understandable representation of results & often produce incomprehensible models. However, neural networks have high acceptance ability for noisy data and high accuracy and are preferable in data mining. In this paper, investigation is made to explore application of Artificial Neural Network in Data mining techniques, the key technology and ways to achieve the data mining based on neural networks are also researched. Given the current state of the art, neural-network deserves a place in the tool boxes of data-mining specialists.