Google scholar arxiv informatics ads IJAIS publications are indexed with Google Scholar, NASA ADS, Informatics et. al.

Call for Paper


January Edition 2023

International Journal of Applied Information Systems solicits high quality original research papers for the January 2023 Edition of the journal. The last date of research paper submission is December 15, 2022.

Application of Data Mining and Knowledge Management for Business Improvement: An Exploratory Study

Lawal, N. T. A., Odeniyi, O. A., Kayode, A. A. Published in Information Sciences

International Journal of Applied Information Systems
Year of Publication: 2015
© 2013 by IJAIS Journal
Download full text
  1. N T A Lawal, O A Odeniyi and A A Kayode. Article: Application of Data Mining and Knowledge Management for Business Improvement: An Exploratory Study. International Journal of Applied Information Systems 8(3):13-19, February 2015. BibTeX

    	author = "Lawal,N.T.A. and Odeniyi,O. A. and Kayode,A. A.",
    	title = "Article: Application of Data Mining and Knowledge Management for Business Improvement: An Exploratory Study",
    	journal = "International Journal of Applied Information Systems",
    	year = 2015,
    	volume = 8,
    	number = 3,
    	pages = "13-19",
    	month = "February",
    	note = "Published by Foundation of Computer Science, New York, USA"


In recent years, there have been a lot of approaches employed by organizations to satisfy their customers and gain competitive advantage. Continuous development of Information System applications is also changing the ways in which businesses are conducted. From scanning barcodes at point of sale (POS) to shopping on the web, businesses are generating large volume of data about products and consumers which are being stored in different data repositories. While a lot of useful knowledge about products, sales and customers that can assist in business decisions are locked away in these databases unexploited. However, the need for organizations to survive in this dynamic business environment depends on how proactive they change these data into useful knowledge which can aid value creation. Presently, customer relationship management and marketing turn out to be the domains which have the potentials to utilize data mining techniques for decision support. This paper examines how business can improve on their performance through utilization of knowledge management (KM) and data mining (DM) applications to manage and support their strategies. Lastly, synergies and challenges of implementation of KM and DM as a tool in business are also critically analysed.


  1. Jashapara, A. 2011. Knowledge Management: An Integrated Approach. Second Edition. Essex: Pearson Education.
  2. Bacon, D. 2002. 'Marketing', In: Klosgen, W. and Jan, Z. (Eds. ), Handbook of Data Mining and Knowledge Discovery. New York: Oxford University Press. Pp. 715-725.
  3. Needle, D. 2004. Business in context: An Introduction to Business and its environment. Fourth Edition. London: Thomson Learning.
  4. Turban, E. , Sharda, R. , Aronson, J. and King, D. 2008. Business Intelligence: A managerial Approach. New Jersey: Pearson Education Inc.
  5. Fayyad, U. (nd) 'Optimize Customer Interaction and Profits–with Advanced Mining Techniques' Available at: http/www. watts-associates. com/docs/article/digimine. pdf [Accessed 27 April 2013].
  6. Shaw, M. J. , Subrumaniam, C. , Tan G. W. and Welge M. E. 2001. 'Knowledge Management and Data Mining for Marketing', Decision Support System, Volume 31, pp. 127-137.
  7. Ayinde, A. Q. , Odeniyi, O. A. and Sarumi, O. A. 2013. 'Mining Parent Socio-Economic Factors to Predict Students' Academic Performance in Osun State College of Technology, Esa-Oke', International Journal of Engineering Research & Technology (IJERT), Volume 2, Issue 12, pp. 1677-1683, ISSN (P): 2278-0181.
  8. Adetunji, A. B. , Ayinde, A. Q. Odeniyi, O. A. and Adewale, J. A. 2013. 'Comparative Analysis of Data Mining Classifiers in Analyzing Clinical Data', International Journal of Engineering Research & Technology (IJERT), Vol. 2, Issue 12, pp. 1671-1676, ISSN (P): 2278-0181.
  9. Hlupic, V. , Pouloudi, A. and Rzevski, G. 2002. 'Towards An Integrated Approach to Knowledge Management: 'Hard', 'Soft' and 'Abstract' Issues', Knowledge and Process Management, Volume 9, Issue 2, pp. 90-102.
  10. Bender, S. and Fish, A. 2000. 'The Transfer of Knowledge and Retention of Expertise: The Continuing Need for Global Assignment', Journal of Knowledge Management, Volume 4, No. 2, pp. 125-137.
  11. Anand, A. and Singh, M. 2011. 'Understanding Knowledge Management: A Literature Review', International Journal of Engineering Science and Technology (IJEST), Volume 3, No. 2, pp. 936-937, ISSN (P): 0975-5462.
  12. Carrillo, P. M. , Robinson, H. S. , Anumba, C. J. and Al-Ghassani, A. M. 2003. 'IMPaKT: A Framework for Linking Knowledge Management to Business Performance'. Electronic Journal of Knowledge Management, Volume 1, Issue 1, pp. 1-12.
  13. Melton, M. 2010. 'An Evaluation of NTWU's Knowledge Management System on Undergraduates Satisfaction and Academic Performance'. Master of Education Thesis, National Taiwan Normal University, Taipei, Taiwan.
  14. Swan J. , Scarbourough, H. and Hislop D. 1999. 'Knowledge Management and Innovations: Networks and Networking', Journal of Knowledge Management, Vol. 3, Issue 4, pp. 262-275.
  15. Debowski, S. 2006. Knowledge Management. Australia: John Wiley and Sons limited.
  16. Greco, M. , Grimadi, M. and Hannandi, M. 2013. 'How to Select Knowledge Management System: A framework to Support Managers', International Journal of Engineering Business Management , Volume 5, Issue 5, pp. 1-11.
  17. An, X. and Wang, W. 2010. 'Knowledge management technologies and applications: A literature Review', Proceedings of International Conference on Advanced Management Science, Beijing, China, 9-11 July 2010, Volume 1, pp. 138-141.
  18. Siliwattananusam, T. and Tuamsuk, K. 2012. 'Data Mining and Its Applications for Knowledge Management: A Literature Review from 2007 to 2012', International Journal of Data Mining and Knowledge Management Process (IJDKP), Volume 2, No. 5, pp. 1-12.
  19. Fayyad, U. , Piatetsky-Shapiro, G. and Smyth, P. 1997, 'From Data Mining to Knowledge Discovery in Databases'. Magazine of American Association for Artificial Intelligence, Volume 17, No. 3 pp. 37-54.
  20. Han, J. , Kamber, M. and Pei, J. 2012. Data Mining Concepts and Techniques. Third Edition. Walthan, MA: Elservier Inc.
  21. Ayinde, A. Q. , Adetunji, A. B. , Odeniyi, O. A. and Bello, M. 2013. 'Performance Evaluation of Naive Bayes and Decision Stump Algorithms in Mining Students' Educational Data', International Journal of Computer Science Issues (IJSCI), Vol. 10, Issue 4, No 1, pp. 147-151.
  22. Fayyad, U. , Piatetsky-Shapiro, G. and Smyth, P. 1996. 'The KDD process for extracting useful knowledge from volumes of data', Communications of the ACM, Volume 39, Issue 11, pp. 27-34.
  23. Cheng, S. , Dai, R. , Xu, W. and Shi, Y. 2006. 'Research on Data Mining and Knowledge Management and Its Applications in China Economic Development: Significance and Trends', International Journal of Information Technology and Decision Making, Volume 5, Issue 4, pp. 585-596.
  24. Nakhaizheizadeh, G. , Steurer, E. and Bartlmae, K. 2002. 'Banking and Finance', In: Klosgen, W. and Jan, Z. ed. (2004) Handbook of Data Mining and Knowledge Discovery, Oxford University Press, New York, NY, pp. 771-780.
  25. Kalkan, V. D. 2008. 'An overall view of knowledge management challenges for global business', Journal of Business Process Management [electronic], Volume 14, No. 3, pp. 390-400.


Data, Data Mining, Knowledge, Knowledge Management, Knowledge Discovery in Databases, Business, Marketing