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.