Abstract:
Data mining and knowledge discovery in Information system research have been
attracting a significant amount of research domains. Information systems are powerful
instruments for organizational problem solving through formal information
processing. Data mining (DM) and knowledge discovery are intelligent tools that help
to accumulate and process data and make use of it. Data mining bridges many
technical areas, including databases, statistics, machine learning, and human-computer
interaction. The set of data mining processes used to extract and verify patterns in data
is the core of the knowledge discovery process. Numerous data mining techniques
have recently been developed to extract knowledge from large databases. In this
survey paper we consider some existing frameworks for data mining, including the
reductionist statistical and probabilistic approaches, database perspective and
inductive databases approach, constructive induction approach and data compression
approach. This research presents methods and techniques together with their
advantages and limitations analyzing what these approaches account in the data
mining research and what they do not.
