Knowledge Discovery and Data Mining

Front Cover
Max A. Bramer
IET, 1999 - 308 pages

Modern computing systems of all kinds accumulate various data at an almost unimaginable rate. Alongside the advances in technology that make such storage possible has grown a realisation that buried within this mass of data there may exist some knowledge of considerable value. This could be information critical for a company's business success or something leading to a scientific or medical discovery or breakthrough. Most data is simply stored and never examined, but machine-learning technology has the potential to extract knowledge of value (i.e. data mining).

This book considers knowledge discovery - which has been defined as 'the extraction of implicit, previously unknown and potentially useful information from data' - and data mining. Six chapters examine technical issues of considerable practical importance to the future development of this field; issues such as how to overcome feature interaction problems, analysis of outliers, rule discovery, the use of background knowledge, temporal patterns and online analysis processing. There then follow six chapters which describe applications in fields as diverse as medical and health information, meteorology, organic chemistry and the electricity supply industry.

The book grew from a colloquium held in 1998 by the IEE, co-sponsored by the British Computer Society Specialist Group on Expert Systems (BCS-SGES), the Society for Artificial Intelligence and Simulation of Behaviour (AISB) and the International Society for Artificial Intelligence and Education (AIED). The chapters have been expanded considerably from papers presented, and all have been fully refereed.

our (AISB) and the International Society for Artificial Intelligence and Education (AIED). The chapters have been expanded considerably from papers presented, and all have been fully refereed.

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Contents

Estimating concept difficulty with cross entropy
3
Analysing oudiers by searching for plausible hypotheses
32
Attributevalue distribution as a technique for increasing
46
Using background knowledge with attributeoriented data mining
64
A development framework for temporal data mining
87
An integrated architecture for OLAP and data mining Z Chen
114
mining
126
Empirical studies of the knowledge discovery approach
139
Direct knowledge discovery and interpretation from a multilayer
160
Discovering knowledge from lowquality meteorological databases
180
A meteorological knowledgediscovery environment
204
Mining the organic compound jungle a functional programming
227
Data mining with neural networks an applied example
240
Index
304
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About the author (1999)

Max Bramer is Professor of Information Technology at the University of Portsmouth, UK. He has been actively involved in research in artificial intelligence since the early 1970s and leads the University of Portsmouth's Artificial Intelligence Research Group. His current research interests include knowledge discovery and data mining and model-based approaches to diagnostic reasoning. He has recently developed the Inducer system, which combines a number of paradigms for the automatic generation of classification rules from examples.He is Chairman of BCS-SGES and is a member of the lEE's Professional Group on Artificial Intelligence.

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