and subject area—for different distribution status in order to explore the differences and how data mining
technologies have developed in this period and to analyze technology tendencies and forecasts of data
mining under the above results. Also, the paper performs the K-S test to check whether the analysis follows
Lotka’s law. Besides, the analysis also reviews the historical literatures to come out technology diffusions
of data mining. The paper provides a roadmap for future research, abstracts technology trends
and forecasts, and facilitates knowledge accumulation so that data mining researchers can save some
time since core knowledge will be concentrated in core categories. This implies that the phenomenon
‘‘success breeds success’’ is more common in higher quality publications.
 2012 Elsevier Ltd. All rights reserved.
1. Introduction
Data mining is an interdisciplinary field that combines artificial
intelligence, database management, data visualization, machine
learning, mathematic algorithms, and statistics. Data mining, also
known as knowledge discovery in databases (KDD) (Chen, Han, &
Yu, 1996; Fayyad, Piatetsky-Shapiro, & Smyth, 1996a), is a rapidly
emerging field. This technology provides different methodologies
for decision-making, problem solving, analysis, planning, diagnosis,

detection, integration, prevention, learning, and innovation
This technology is motivated by the need of new techniques to
help analyze, understand or even visualize the huge amounts of
stored data gathered from business and scientific applications. It
is the process of discovering interesting knowledge, such as patterns,
associations, changes, anomalies and significant structures
from large amounts of data stored in databases, data warehouses,
or other information repositories. It can be used to help companies
to make better decisions to stay competitive in the marketplace.
The major data mining functions that are developed in commercial
and research communities include summarization, association,
classification, prediction and clustering. These functions can be
implemented using a variety of technologies, such as database-oriented
techniques, machine learning and statistical techniques
(Fayyad, Piatetsky-Shapiro, & Smyth, 1996b).
Data mining was defined by Turban, Aronson, Liang, and Sharda
(2007, p.305) as a process that uses statistical, mathematical, artificial
intelligence and machine-learning techniques to extract and
identify useful information and subsequently gain knowledge from
large databases. In an effort to develop new insights into practiceperformance
relationships, data mining was used to investigate
improvement programs, strategic priorities, environmental factors,
manufacturing performance dimensions and their interactions
(Hajirezaie, Husseini, Barfourosh, et al., 2010). Berson, Smith, and
Thearling (2000), Lejeune (2001), Ahmed (2004) and Berry and Linoff
(2004) also defined data mining as the process of extracting or
detecting hidden patterns or information from large databases.
With an enormous amount of customer data, data mining technology
can provide business intelligence to generate new opportunities