ICAN Canada Decision Making Optimization through Data Mining Prof Oyedokun.pptx

Decision Making Optimization through Data Mining

Being a Paper Presented at the 9th Accountant Conference of the Institute of Chartered Accountant of Nigeria (ICAN) Canada District from July 16 – 20, 2024.

1. Decision Making Optimization through Data Mining Being a Paper Presented at the 9th Accountant Conference of the Institute of Chartered Accountant of Nigeria (ICAN) Canada District from July 16 – 20, 2024. Prof. Godwin Emmanuel Oyedokun Professor of Accounting and Financial Development Department of Management & Accounting Faculty of Management and Social Sciences Lead City University, Ibadan, Nigeria Principal Partner; Oyedokun Godwin Emmanuel & Co (Accountants, Tax Practitioners & Forensic Auditors)

2. ND (Fin), HND (Acct.), BSc. (Acct. Ed), BSc (Fin.), LLB., LLM, MBA (Acct. & Fin.), MSc. (Acct.), MSc. (Bus & Econs ), MSc. (Fin), MSc. ( Econs ), Ph.D. (Acct), Ph.D. (Fin), Ph.D. (FA), CICA, CFA, CFE, CIPFA, CPFA, CertIFR , ACS, ACIS, ACIArb , ACAMS, ABR, IPA, IFA, MNIM, FCA, FCTI, FCIB, FCNA, FCFIP, FCE, FERP, FFAR, FPD-CR, FSEAN, FNIOAIM, FCCrFA , FCCFI, FICA, FCECFI, JP Prof. Godwin Emmanuel Oyedokun Professor of Accounting and Financial Development Department of Management & Accounting Faculty of Management and Social Sciences Lead City University, Ibadan, Nigeria Principal Partner; Oyedokun Godwin Emmanuel & Co (Accountants, Tax Practitioners & Forensic Auditors)

3. Decision Making Optimization through Data Mining 3

4. 4

5. Objectives 5

6. Contents 6

7. Introduction 7

8. 8

9. Overview of Decision Making and Decision Making Optimization 9

10. Theories and Models of Decision Making 10

11. Factors Influencing Decision Making 11

12. Ethical Considerations in Decision Making 12

13. Traditional Decision-Making 13

14. Steps in Traditional Decision-Making 14

15. Challenges of Traditional Decision-Making Method /1 15

16. 16

17. Challenges of Traditional Decision-Making Method /2 17

18. The Impact of Technology on Decision Making 18

19. Decision Making Optimization 19

20. 20

21. Strategies for Decision Making Optimization 21

22. Challenges in Decision Making Optimization 22

23. 23

24. Overview of Data Mining 24

25. T he Science behind Data Mining 25

26. Methodologies/Techniques in Data Mining 26

27. Applications of Data Mining 27

28. Challenges in Data Mining 28

29. 29

30. Data Mining Tools, Manufacturers, Cost and Special Features /1 WEKA (Waikato Environment for Knowledge Analysis) RapidMiner 30

31. Data Mining Tools, Manufacturers, Cost and Special Features /2 Orange KNIME (Konstanz Information Miner) 31

32. Data Mining Tools, Manufacturers, Cost and Special Features /3 SAS Enterprise Miner IBM SPSS Modeler 32

33. Data Mining Tools, Manufacturers, Cost and Special Features /4 Microsoft Azure Machine Learning Alteryx 33

34. 34

35. Data Warehousing Software /1 35

36. Data Warehousing Software /2 36

37. 37

38. Decision Making Optimization through Data Mining /1 38

39. Decision Making Optimization through Data Mining /2 39

40. Examples of Data Mining and Their Application to Decision-Making 40

41. Examples of Data Mining and Their Application to Decision-Making 41

42. How Data Mining Improves Decision-Making /1 42

43. 43

44. How Data Mining Improves Decision-Making /2 44

45. Importance of Data Mining in Decision-Making Optimization 45

46. Ethical Considerations of Using Data Mining for Decision-Making /1 46

47. Ethical Considerations of Using Data Mining for Decision-Making /2 47

48. Monitoring and Appraising the Effect of Data Mining on Decision-Making 48

49. Monitoring and Appraising the Effect of Data Mining on Decision-Making 49

50. Decision Optimization Techniques in Data Mining /1 50

51. 51

52. Decision Optimization Techniques in Data Mining /2 52

53. Decision Optimization Techniques in Data Mining /3 53

54. Decision Optimization Techniques in Data Mining /4 54

55. Steps in Decision Optimization Techniques in Data Mining 55

56. Interactions between Data Mining and Optimal Decision-Making Strategies /1 Data Mining as a Foundation for Decision-Making Decision Optimization Techniques Enhanced by Data Mining 56

57. Interactions between Data Mining and Optimal Decision-Making Strategies /2 Continuous Improvement and Adaptation Integration in Decision Support Systems (DSS) and Business Intelligence (BI) 57

58. Interactions between Data Mining and Optimal Decision-Making Strategies /3 Data Mining Techniques Supporting Decision Optimization 58

59. Example Scenario: Customer Retention Strategy 59

60. 60

61. Conclusion 61

62. Recommendations 62

63. References 63 Bertsimas, D., & Thiele, A. (2006). Robust and data-driven optimization: Modern decision making under uncertainty. INFORMS Journal on Computing, 18(3), 248-276. Bolton, R. J., & Hand, D. J. (2002). Statistical fraud detection: A review. Statistical Science, 17(3), 235-255. Brynjolfsson , E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies . W. W. Norton & Company. Buckinx , W., & Van den Poel , D. (2005). Customer base analysis: Partial defection of behaviourally loyal clients in a non-contractual FMCG retail setting. European Journal of Operational Research, 164(1), 252-268. Chandola , V., Banerjee, A., & Kumar, V. (2009). Anomaly detection: A survey. ACM Computing Surveys (CSUR), 41 (3), 1-58. https://doi.org/10.1145/1541880.1541882 Chaudhuri , S., Dayal , U., & Narasayya , V. (2011). An overview of business intelligence technology. Communications of the ACM, 54(8), 88-98. Chen, H., Chiang, R. H., & Storey , V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 36(4), 1165-1188. Davenport, T. H., & Harris, J. G. (2007). Competing on analytics: The new science of winning . Harvard Business Review Press. De Bono, E. (1985). Six thinking hats . Little, Brown, and Company . Gao , J., Liu, Y., Li, X., Shen , H., & Bo, L. (2015). Online anomaly detection for multi-source log data in cloud computing. Frontiers of Computer Science, 9(2), 270-282. Goleman , D. (1995). Emotional intelligence: Why it can matter more than IQ . Bantam Books. Halpern, D. F. (1998). Teaching critical thinking for transfer across domains: Dispositions, skills, structure training, and metacognitive monitoring . American Psychologist, 53(4), 449-455. Han, J., Pei, J., & Kamber , M. (2011). Data mining: Concepts and techniques . Elsevier. Hu, M., Liu, B., & Cheng, J. (2013). Opinion extraction and sentiment classification on the web: A survey. ACM Transactions on Internet Technology (TOIT), 13(2), 1-41. Humphrey, A. S. (1966). SWOT analysis . SRI International. Janis, I. L. (1972). Victims of groupthink: A psychological study of foreign-policy decisions and fiascoes . Houghton Mifflin.

64. References 64 Kahneman , D., Lovallo , D., & Sibony , O. (2011). Before you make that big decision. Harvard Business Review, 89 (6), 50-60. Kant, I. (1993). Grounding for the metaphysics of morals (J. W. Ellington, Trans.). Hackett Publishing Company. (Original work published 1785) Klein, G. (1998). Sources of power: How people make decisions . MIT Press. Kotter , J. P. (1996). Leading change . Harvard Business Review Press. Kumar, V., & Sastry , S. (2012). Data mining and its applications . Springer. Li, T., Zhang, Z., Yuan, S., & Dai, Q. (2015). Social media mining: A new framework and outlook. IEEE Transactions on Information Technology in Biomedicine, 21(1), 38-50. Mill, J. S. (1863). Utilitarianism . Parker, Son, and Bourn, West Strand. Ngai , E. W., Hu, Y., Wong, Y. H., Chen, Y., & Sun, X. (2011). The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature. Decision Support Systems, 50(3), 559-569. Ngai , E. W., Xiu , L., & Chau , D. C. (2009). Application of data mining techniques in customer relationship management: A literature review and classification. Expert Systems with Applications, 36(2), 2592-2602. Nicholson, N., Soane , E., Fenton- O'Creevy , M., & Willman , P. (2005). Personality and domain-specific risk taking . Journal of Risk Research, 8(2), 157-176. Porter, M. E. (1980). Competitive Strategy: Techniques for Analyzing Industries and Competitors. Free Press. Power, D. J. (2002). Decision support systems: Concepts and resources for managers . Greenwood Publishing Group. Provost, F., & Fawcett, T. (2013). Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking . O'Reilly Media. Redman, T. C. (2013). Data-driven: Profiting from your most important business asset. Harvard Business Review Press . Russell, S., & Norvig , P. (2016). Artificial intelligence: A modern approach (3rd ed.). Pearson Education Limited. Saaty , T. L. (1980). The analytic hierarchy process: Planning, priority setting, resource allocation . McGraw-Hill. Schafer, J. B., Frankowski , D., Herlocker , J., & Sen , S. (2007). Collaborative filtering recommender systems. In The Adaptive Web (pp. 291-324). Springer, Berlin, Heidelberg .

65. References 65 Simon, H. A. (1957). Models of man: Social and rational . John Wiley and Sons, Inc. Simon, H. A. (1979). Rational decision making in business organizations . American Economic Review, 69(4), 493-513. Simon, H. A. (1997). Administrative behavior: A study of decision-making processes in administrative organizations (4th ed.). Free Press. Thomas, L. C., Edelman, D. B., & Crook, J. N. (2002). Credit Scoring and Its Applications. SIAM. Tsai, C. F., & Hung, C. S. (2015). Mining customers' knowledge for improving the quality of product recommendations in B2C e-commerce. International Journal of Information Management, 35(3), 365-378. Tversky , A., & Kahneman , D. (1974). Judgment under uncertainty: Heuristics and biases . Science, 185(4157), 1124-1131. Witten, I. H., Frank, E., & Hall, M. A. (2011). Data mining: Practical machine learning tools and techniques . Morgan Kaufmann. Zhang, Z., & Wang, Y. (2019). Data mining for the Internet of Things: Literature review and challenges. International Journal of Distributed Sensor Networks, 15 (11), 1-15. https://doi.org/10.1177/1550147719888337. Zhao, Y., & Zhang, Y. (2008). Comparison of decision tree methods for finding active objects. Advances in Space Research, 41(12), 1955-1961.

66. Prof. Godwin Emmanuel Oyedokun Professor of Accounting & Financial Development Lead City University, Ibadan, Nigeria Principal Partner; Oyedokun Godwin Emmanuel & Co (Accountants , Tax Practitioners & Auditors) [email protected]; [email protected] [phone] & 2348055863944 66