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A review of data mining techniques and applications

By R. Pruengkarn, K.W. Wong and C.C. Fung

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

Publisher: Fuji Technology Press Co. Ltd.
Year: 2017
DOI identifier: 10.20965/jaciii.2017.p0031
OAI identifier: oai:researchrepository.murdoch.edu.au:35502
Provided by: Research Repository
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