An overview of the use of neural networks for data mining tasks

In the recent years the area of data mining has experienced a considerable demand for technologies to extract knowledge from large and complex data sources. There is a substantial commercial interest as well as research activities in the area that aim to develop new and improved approaches for extracting information, relationships, and patterns from datasets. Artificial Neural Networks (NN) are popular biologically inspired intelligent methodologies, whose classification, prediction and pattern recognition capabilities have been utilised successfully in many areas, including science, engineering, medicine, business, banking, telecommunication, and many other areas. This paper highlights the implementation of NN, trained by supervised and unsupervised learning, for pattern recognition, classification, prediction and cluster analysis, from a data mining perspective, and discusses some examples of their usage in bioinformatics and financial data analysis tasks. Comment [JW1]: Remember that you are writing for an interdisciplinary audience: please be sure to discuss interdisciplinary themes, issues, debates, etc. where appropriate. PLEASE READ ALL ‘COMMENT’ TEXT BELOW BEFORE PREPARING YOUR ARTICLE. Refer to the Guide for Authors for more detailed instructions, or contact [email protected]. Comment [JW2]: Regardless of what type of article you are submitting, please bear in mind that WIREs Data Mining and Knowledge Discovery is a forum for review articles, rather than primary literature describing the results of original research. Page 1 of 27 John Wiley & Sons WIREs Data Mining and Knowledge Discovery 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 or Peer Rview Introduction Currently, ubiquitous computer systems record our habits, decisions, behaviours and lives daily. We use debit and credit cards to pay and club cards to save on our shopping. We are recorded on CCTV cameras and leave our information in the World Wide Web using applications such as email, blogs, twitter or social networks. But it is not only us, the individuals that constantly generate data records in various large data bases every day, it is also scientific applications that record large amounts of data constantly. For example, the NASA’s system of earth orbiting satellites which generates a terrabyte of data every day 61 ; the human genome project 1 that stores the genetic blueprint of our bodies; molecular simulations that study the dynamic behaviour of molecules and generate large amounts of data stored in distributed data warehouses 53 . Considerable advances in storage technology made it possible to store all this information at relatively low cost. However, just having the data available in databases does not make us more knowledgeable. We may be ‘information rich’, but still ‘knowledge poor’ 15 and the gap between recording information and understanding it is