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JACIII Vol.21 No.1 pp. 31-48
doi: 10.20965/jaciii.2017.p0031
(2017)

Invited Paper:

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A Review of Data Mining Techniques and Applications

Ratchakoon Pruengkarn, Kok Wai Wong, and Chun Che Fung

School of Engineering and Information Technology, Murdoch University
Perth, Australia

Received:
October 24, 2016
Accepted:
December 19, 2016
Published:
January 20, 2017
Creative Commons License
Keywords:
data mining, data mining techniques, data mining application, big data
Abstract
Data mining is the analytics and knowledge discovery process of analyzing large volumes of data from various sources and transforming the data into useful information. Various disciplines have contributed to its development and is becoming increasingly important in the scientific and industrial world. This article presents a review of data mining techniques and applications from 1996 to 2016. Techniques are divided into two main categories: predictive methods and descriptive methods. Due to the huge number of publications available on this topic, only a selected number are used in this review to highlight the developments of the past 20 years. Applications are included to provide some insights into how each data mining technique has evolved over the last two decades. Recent research trends focus more on large data sets and big data. Recently there have also been more applications in area of health informatics with the advent of newer algorithms.
Cite this article as:
R. Pruengkarn, K. Wong, and C. Fung, “A Review of Data Mining Techniques and Applications,” J. Adv. Comput. Intell. Intell. Inform., Vol.21 No.1, pp. 31-48, 2017.
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