Neural Networks for Prediction and Classification

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Author(s): Kate A. Smith (Monash University, Australia)
Copyright: 2005
Pages: 5
Source title: Encyclopedia of Data Warehousing and Mining
Source Author(s)/Editor(s): John Wang (Montclair State University, USA)
DOI: 10.4018/978-1-59140-557-3.ch164

Keywords: Data Mining and Databases / Data Warehousing / Information Science Reference / Library & Information Science

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Abstract

Neural networks are simple computational tools for examining data and developing models that help to identify interesting patterns or structures. The data used to develop these models is known as training data. Once a neural network has been exposed to the training data, and has learnt the patterns that exist in that data, it can be applied to new data thereby achieving a variety of outcomes. Neural networks can be used to: • learn to predict future events based on the patterns that have been observed in the historical training data; • learn to classify unseen data into pre-defined groups based on characteristics observed in the training data; • learn to cluster the training data into natural groups based on the similarity of characteristics in the training data.

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