IDEAS home Printed from https://ideas.repec.org/a/aif/journl/v4y2020i7p46-51.html
   My bibliography  Save this article

Reviewing Data Mining as an enabling technology for BI

Author

Listed:
  • Mustafa Nizamul Aziz

    (Senior Lecturer, East West University, Dhaka, Bangladesh.)

  • A.K.M. Monzurul Islam

    (Independent University, Bangladesh.)

Abstract

Data mining (DM) is the process of discovering or “mining” new knowledge from large databases and applying it to decision making. In recent times, companies have been using a wide range of data mining techniques to better understand their customers and their performance and solve complex business problems. Data mining is a way to develop Business Intelligence (BI) from data that an organization collects, organizes, and stores. The purpose of this paper is to review Data Mining as an enabling technology for Business Intelligence. Documents analysis was used as the data collection method and a qualitative approach was used for data analysis in the study.

Suggested Citation

  • Mustafa Nizamul Aziz & A.K.M. Monzurul Islam, 2020. "Reviewing Data Mining as an enabling technology for BI," International Journal of Science and Business, IJSAB International, vol. 4(7), pages 46-51.
  • Handle: RePEc:aif:journl:v:4:y:2020:i:7:p:46-51
    as

    Download full text from publisher

    File URL: https://ijsab.com/wp-content/uploads/567.pdf
    Download Restriction: no

    File URL: https://ijsab.com/volume-4-issue-7/3060
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. David J. Hand & Heikki Mannila & Padhraic Smyth, 2001. "Principles of Data Mining," MIT Press Books, The MIT Press, edition 1, volume 1, number 026208290x, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Chnar Mustaf Mohammed & Shavan Askar, 2021. "Machine Learning for IoT HealthCare Applications: A Review," International Journal of Science and Business, IJSAB International, vol. 5(3), pages 42-51.
    2. Shavan Askar & Faris Keti, 2021. "Performance Evaluation of Different SDN Controllers: A Review," International Journal of Science and Business, IJSAB International, vol. 5(6), pages 67-80.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Fernandez del Pozo, J. A. & Bielza, C. & Gomez, M., 2005. "A list-based compact representation for large decision tables management," European Journal of Operational Research, Elsevier, vol. 160(3), pages 638-662, February.
    2. Le, Hong Hanh & Viviani, Jean-Laurent, 2018. "Predicting bank failure: An improvement by implementing a machine-learning approach to classical financial ratios," Research in International Business and Finance, Elsevier, vol. 44(C), pages 16-25.
    3. Li, Hui & Sun, Jie, 2009. "Hybridizing principles of the Electre method with case-based reasoning for data mining: Electre-CBR-I and Electre-CBR-II," European Journal of Operational Research, Elsevier, vol. 197(1), pages 214-224, August.
    4. Xue, Puning & Zhou, Zhigang & Fang, Xiumu & Chen, Xin & Liu, Lin & Liu, Yaowen & Liu, Jing, 2017. "Fault detection and operation optimization in district heating substations based on data mining techniques," Applied Energy, Elsevier, vol. 205(C), pages 926-940.
    5. Min-feng Lee & Guey-shya Chen & Shao-pin Lin & Wei-jie Wang, 2022. "A Data Mining Study on House Price in Central Regions of Taiwan Using Education Categorical Data, Environmental Indicators, and House Features Data," Sustainability, MDPI, vol. 14(11), pages 1-15, May.
    6. Caruso, Germán & Scartascini, Carlos & Tommasi, Mariano, 2015. "Are we all playing the same game? The economic effects of constitutions depend on the degree of institutionalization," European Journal of Political Economy, Elsevier, vol. 38(C), pages 212-228.
    7. MARIA Dan Stefan, 2009. "Improving The Quality Of The Decision Making By Using Business Intelligence Solutions," Annals of Faculty of Economics, University of Oradea, Faculty of Economics, vol. 4(1), pages 996-1000, May.
    8. M. Almiñana & L. Escudero & A. Pérez-Martín & A. Rabasa & L. Santamaría, 2014. "A classification rule reduction algorithm based on significance domains," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(1), pages 397-418, April.
    9. Silvia FIGINI & Ron S. KENETT & Silvia SALINI, 2010. "Integrating operational and financial risk assessments," Departmental Working Papers 2010-02, Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano.
    10. Tasadduq Imam, 2021. "Model selection for one‐day‐ahead AUD/USD, AUD/EUR forecasts," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(2), pages 1808-1824, April.
    11. Onur Doğan & Hakan Aşan & Ejder Ayç, 2015. "Use Of Data Mining Techniques In Advance Decision Making Processes In A Local Firm," European Journal of Business and Economics, Central Bohemia University, vol. 10(2), pages 6821:10-682, January.
    12. Doumpos, Michael & Zopounidis, Constantin, 2011. "Preference disaggregation and statistical learning for multicriteria decision support: A review," European Journal of Operational Research, Elsevier, vol. 209(3), pages 203-214, March.
    13. Nieminen, Paavo & Pölönen, Ilkka & Sipola, Tuomo, 2013. "Research literature clustering using diffusion maps," Journal of Informetrics, Elsevier, vol. 7(4), pages 874-886.
    14. Patricia E. N. Lutu & Andries P. Engelbrecht, 2013. "Base Model Combination Algorithm for Resolving Tied Predictions for K -Nearest Neighbor OVA Ensemble Models," INFORMS Journal on Computing, INFORMS, vol. 25(3), pages 517-526, August.
    15. Pooyan Ramezani Besheli & Mehdi Zare & Ramezan Ramezani Umali & Gholamreza Nakhaeezadeh, 2015. "Zoning Iran based on earthquake precursor importance and introducing a main zone using a data-mining process," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 78(2), pages 821-835, September.
    16. Adrien Jamain & David Hand, 2008. "Mining Supervised Classification Performance Studies: A Meta-Analytic Investigation," Journal of Classification, Springer;The Classification Society, vol. 25(1), pages 87-112, June.
    17. Fang, Jiali & Jacobsen, Ben & Qin, Yafeng, 2014. "Predictability of the simple technical trading rules: An out-of-sample test," Review of Financial Economics, Elsevier, vol. 23(1), pages 30-45.
    18. Marcin Chlebus & Zuzanna Osika, 2020. "Comparison of tree-based models performance in prediction of marketing campaign results using Explainable Artificial Intelligence tools," Working Papers 2020-15, Faculty of Economic Sciences, University of Warsaw.
    19. Silvia Figini & Roberto Savona & Marika Vezzoli, 2016. "Corporate Default Prediction Model Averaging: A Normative Linear Pooling Approach," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 23(1-2), pages 6-20, January.
    20. Mircea Andrei SCRIDON, 2008. "Understanding Customers - Profiling And Segmentation," Management and Marketing Journal, University of Craiova, Faculty of Economics and Business Administration, vol. 6(1), pages 175-184, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:aif:journl:v:4:y:2020:i:7:p:46-51. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Farjana Rahman (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.