Nowadays, some of the intersting roles of human life are data, information, and knowledge. Analyzing and modelling of big data have been required by data massive storehouses together with the rapid technologies growth to predict and analyze the future trends of information. Methodologies and techniques, which are employed into diverse information systems scope, are needed for detection of knowing in the databases. The technology which extracts advantageous information to discover knowledge is called Data Mining. Data mining, it has been defined as discovery of knowledge in data (KDD), it is the disclosure of modalities procedures and other valuable information from considerable sets of data. It has been a tremendous progress in machine learning, artificial agent systems, and decision-making in the expert systems. In the last decades, most of the techniques and applications has been surveyed via the researchers. Those techniques and applications are utilized in distinct areas in daily life like industrialization, education, engineering, commerce and business. Searching last years researches about the review of the most techniques and trends of data mining in multiple areas was the method which is followed in this paper. It has discovered in the learning field as diffusing data mining for educating activities, improvement quality of tasks into manufacturing field, text mining as a technique into research databases and so on. This study collectes a summary of information about the basci concept of Data Mining and its technques which other researchers may need to start their studies in Data Mining field.
Abu Saa, A., Al-Emran, M., & Shaalan, K. (2019). Factors affecting students’ performance in higher education: a systematic review of predictive data mining techniques. Technology, Knowledge and Learning, 24(4), 567-598.
Chamizo-Gonzalez, J., Cano-Montero, E. I., Urquia-Grande, E., & Munoz-Colomina, C. I. (2015). Educational data mining for improving learning outcomes in teaching accounting within higher education. The International Journal of Information and Learning Technology.
Dogan, A., & Birant, D. (2021). Machine learning and data mining in manufacturing. Expert Systems with Applications, 166, 114060.
Elmasri, R., & Navathe, S. (2014). Fundamentals of database systems (Vol. 7). Pearson.
Freitas, A. A. (2002). Data mining and knowledge discovery with evolutionary algorithms. Springer Science & Business Media.
Gillenson, M. L. (2008). Fundamentals of database management systems. John Wiley & Sons.
Han, J., Pei, J., & Kamber, M. (2011). Data mining: concepts and techniques. Elsevier.
Hand, D. J., Mannila, H., & Smyth, P. (2001). Principles of data mining. Cambridge, MA: Bradford Book.
Islam, M. S., Hasan, M. M., Wang, X., Germack, H. D., & Noor-E-Alam, M. (2018). A systematic review on healthcare analytics: application and theoretical perspective of data mining. In Healthcare (Vol. 6, No. 2, p. 54). MDPI.
Issad, H. A., Aoudjit, R., & Rodrigues, J. J. (2019). A comprehensive review of Data Mining techniques in smart agriculture. Engineering in Agriculture, Environment and Food, 12(4), 511-525.
Kantardzic, M. (2011). Data mining: concepts, models, methods, and algorithms. John Wiley & Sons.
Koksal, G., Batmaz, I., & Testik, M. C. (2011). A review of data mining applications for quality improvement in manufacturing industry. Expert systems with Applications, 38(10), 13448-13467.
Liao, S. H., Chu, P. H., & Hsiao, P. Y. (2012). Data mining techniques and applications–A decade review from 2000 to 2011. Expert systems with applications, 39(12), 11303-11311.
Nagarkar, S. P., & Kumbhar, R. (2015). Text mining: an analysis of research published under the subject category ‘Information Science Library Science’in Web of Science Database during 1999-2013. Library Review.
Peña-Ayala, A. (2014). Educational data mining: A survey and a data mining-based analysis of recent works. Expert systems with applications, 41(4), 1432-1462.
Reddy, D. L. C. (2011). A review on data mining from past to the future. International Journal of Computer Applications, 975, 8887.
Rodrigues, M. W., Isotani, S., & Zarate, L. E. (2018). Educational Data Mining: A review of evaluation process in the e-learning. Telematics and Informatics, 35(6), 1701-1717.
Thalheim, B. (2013). Entity-relationship modeling: foundations of database technology. Springer Science & Business Media.
In-Text Citation: (Mostafa & Mahmoud, 2022)
To Cite this Article: Mostafa, A. A. N., & Mahmoud, H. E. A. (2022). Review of Data Mining Concept and its Techniques. International Journal of Academic Research in Business and Social Sciences. 12(6), 599– 607.
Copyright: © 2022 The Author(s)
Published by Knowledge Words Publications (www.kwpublications.com)
This article is published under the Creative Commons Attribution (CC BY 4.0) license. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this license may be seen at: http://creativecommons.org/licences/by/4.0/legalcode
