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.
