• P-ISSN 0974-6846 E-ISSN 0974-5645

Indian Journal of Science and Technology


Indian Journal of Science and Technology

Year: 2021, Volume: 14, Issue: 5, Pages: 508-518

Original Article

Role of data mining techniques in business

Received Date:18 April 2020, Accepted Date:20 November 2021, Published Date:19 February 2021


Objective: The main objective is to elaborate and discuss different techniques used in data mining, to analyze different strategies of data mining to make improvements, and to find more powerful mining techniques for the betterment of the business. Methods: Multiple techniques and strategies of data mining are used to improve the business. We employed the data warehouse methods for the improvements of the business using Business Intelligence (BI) and Business Analytics (BA) along with their types and instruments. We are also discussing some tools used for data mining or ordering organizational data. Findings: We employed Business Intelligence-(BI) and Business Analytics-(BA) techniques for the improvement of the business. Earlier, there were only four (Regression, Classification, Association, and Clustering) techniques that were used for business improvements. It is found that Crawler is the best tool for BI or BA data mining. Novelty : This study analyzed that, BI and BA are the best ways used for data mining, data ordering, or format of data in business. Earlier, these ways were not in use for data mining. Data mining may be the best approach to improve the business.

Keywords: Business Intelligence (BI); Business Analytics Data Mining (BADM); Data Warehouse (DW); Knowledge Discovery (KDD) Customer Relationship Management (CRM); Enterprise Resource Planning (ERP)


  1. Tan PN, Steinbach M, Kumar V. Introduction to data mining. Pearson Education India. 2016.
  2. Aggarwal CC. Data mining: the textbook. Springer. 2015.
  3. Han J, Pei J, Kamber M. Data mining: concepts and techniques. Elsevier. 2011.
  4. Hussain R, Lee J, Zeadally S. Trust in VANET: A Survey of Current Solutions and Future Research Opportunities. IEEE Transactions on Intelligent Transportation Systems. 2020;p. 1–19. Available from: https://dx.doi.org/10.1109/tits.2020.2973715
  5. Taherkordi A, Zahid F, Verginadis Y, Horn G. Future Cloud Systems Design: Challenges and Research Directions. IEEE Access. 2018;6:74120–74150. Available from: https://dx.doi.org/10.1109/access.2018.2883149
  6. Apte C, Dietrich B, Fleming M. Business leadership through analytics. IBM Journal of Research and Development. 2012;56(6):1–5. Available from: https://dx.doi.org/10.1147/jrd.2012.2214555
  7. Kim SM, Ha YG. Automated discovery of small business domain knowledge using web crawling and data mining. 2016 International Conference on Big Data and Smart Computing (BigComp). 2016;p. 481–484. Available from: https://ieeexplore.ieee.org/abstract/document/7425974
  8. Leskovec J, Rajaraman A, Ullman JD. Mining of massive data sets. Cambridge university press. 2020.
  9. Vashani H, Sullivan J, Asmar ME. DB 2020: Analyzing and Forecasting Design-Build Market Trends. Journal of Construction Engineering and Management. 2016;142(6). Available from: https://dx.doi.org/10.1061/(asce)co.1943-7862.0001113
  10. Schoier G, Borruso G. A methodology for dealing with spatial big data. International Journal of Business Intelligence and Data Mining. 2017;12(1):1. Available from: https://dx.doi.org/10.1504/ijbidm.2017.082705
  11. Kraus M, Feuerriegel S, Oztekin A. Deep learning in business analytics and operations research: Models, applications and managerial implications. European Journal of Operational Research. 2020;281(3):628–641. Available from: https://dx.doi.org/10.1016/j.ejor.2019.09.018
  12. Abbas I, Muneer U. The Prediction of death causes using regression models and moving averages. International Journal of Data Science and Advanced Analytics. 2019;1(1):39–46. Available from: http://ijdsaa.com/index.php/welcome/article/view/69
  13. Mazimpaka JD, Timpf S. Trajectory data mining: A review of methods and applications. Journal of Spatial Information Science. 2016;(13) 61–99. Available from: https://dx.doi.org/10.5311/josis.2016.13.263
  14. Thomas DM, Mathur S. Data analysis by web scraping using python. 2019 3rd International conference on Electronics, Communication and Aerospace Technology (ICECA). 2019;p. 450–454. Available from: https://ieeexplore.ieee.org/abstract/document/8822022
  15. Saurkar AV, Pathare KG, Gode SA. An Overview On Web Scraping Techniques And Tools. International Journal on Future Revolution in Computer Science & Communication Engineering. 2018;4(4):363–367.


© 2021 Abbas et al.This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)


Subscribe now for latest articles and news.