Abstract
Despite the rapid development, the field of data mining and knowledge discovery (DMKD) is still vaguely defined and lack of integrated descriptions. This situation causes difficulties in teaching, learning, research, and application. This paper surveys a large collection of DMKD literature to provide a comprehensive picture of current DMKD research and classify these research activities into high-level categories using grounded theory approach; it also evaluates the longitudinal changes of DMKD research activities during the last decade.
Highlights
In the last two decades, the advances in digital data collection devices and data storage technology allow companies and organizations to store up huge amounts of data
This paper presented a descriptive framework for the field of Data mining and knowledge discovery (DMKD) using grounded theory
A large collection of DMKD publications was analyzed following the analytical procedures of grounded theory
Summary
In the last two decades, the advances in digital data collection devices and data storage technology allow companies and organizations to store up huge amounts of data. Underneath these data are great opportunities for knowledge discovery. In 1989, the first one-day KDD workshop was held at International Joint Conference on Artificial Intelligence (IJCAI) in Detroit with nine papers presented. In 2007, there are 17 data mining conferences.[1] DMKD has attracted interest from various fields, including databases, machines learning, pattern recognition, artificial intelligence, statistics, data visualization, mathematics and optimization,[2] and economics.[3]
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