要旨
The current trend of increasing capabilities in data
generation and collection has resulted in an urgent need for
data mining applications, also called knowledge discovery
in databases. This paper identifies and examines the issues
involved in extracting useful grains of knowledge from
large amounts of data.
It describes a framework to categorise data mining systems.
The author also gives an overview of the issues pertaining
to data pre processing, as well as various information
gathering methodologies and techniques. The paper covers
some popular tools such as classification, clustering, and
generalisation. A summary of statistical and machine
learning techniques used currently is also provided.
