USE OF DATA MINING TECHNIQUES IN ADVANCE DECISION MAKING PROCESSES IN A LOCAL FIRM

Onur Doğan, Hakan  Aşan, Ejder Ayç

Abstract


In today’s competitive world, organizations need to make the right decisions to prolong their existence. Using non-scientific methods and making emotional decisions gave way to the use of scientific methods in the decision making process in this competitive area. Within this scope, many decision support models are still being developed in order to assist the decision makers and owners of organizations. It is easy to collect massive amount of data for organizations, but generally the problem is using this data to achieve economic advances. There is a critical need for specialization and automation to transform the data into the knowledge in big data sets. Data mining techniques are capable of providing description, estimation, prediction, classification, clustering, and association. Recently, many data mining techniques have been developed in order to find hidden patterns and relations in big data sets. It is important to obtain new correlations, patterns, and trends, which are understandable and useful to the decision makers. There have been many researches and applications focusing on different data mining techniques and methodologies.

In this study, we aim to obtain understandable and applicable results from a large volume of record set that belong to a firm, which is active in the meat processing industry, by using data mining techniques. In the application part, firstly, data cleaning and data integration, which are the first steps of data mining process, are performed on the data in the database. With the aid of data cleaning and data integration, the data set was obtained, which is suitable for data mining. Then, various association rule algorithms were applied to this data set. This analysis revealed that finding unexplored patterns in the set of data would be beneficial for the decision makers of the firm. Finally, many association rules are obtained, which are useful for decision makers of the local firm.

 


Keywords


Decision making, Data mining, Decision tree algorithms

Full Text:

PDF

References


Berry, M. J. A., & Linoff, G. (1997). Data Mining Techniques for Marketing, Sales, and Customer Support, Wiley, New York.

Bounsaythip, C., & Rinta-Runsala, E. (2001). Overview of Data Mining For Customer Behavior Modeling. VTT Information Technology Research Report, Version:1.

Frawley, W. J., Piatetsky-Shapiro, G., & Matheus, C. J. (1992). Knowledge discovery in databases: an overview. AI Magazine, 13(3), 57–70.

Freund, Y., & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 1, 119–139.

Han, J., & Kamber, M. (2001). Data Mining, Concepts and Techniques. Morgan Kaufmann Publishers.

Hand, D, Mannila, H., & Smyth, P. (2001). Principles of Data Mining. London, UK: The MIT Press.

Hunt, E. B., Marin, J., & Stone, P. J. (1966). Experiments in induction. New York: Academic Press.

Kabra, R., & Cichkar, S. (2011). Performance Prediction of Engineering Students using Decision Tree. International Journal of Computer Applications, 36, 11.

Kass, G. (1980). An Exploratory Technique for Investigating Large Quantities of Categorical Data. Applied Statistics, 29, 119–127.

Mehta, M, Agrawal, R., & Rissanen, J. (1996). SLIQ: A fast scalable classifier for data mining. Proceedings International Conference on Extending Database Technology, 18–32.

Pang S., & Gong J. (2009). C5.0 Classification Algorithm and Application on Individual Credit Evaluation of Banks. System Engineering- Theory &Practice, 29(12), 94-104.

Patterson, A., & Niblett, T. (1983). ACLS user manual. Glasgow: Intelligent Terminals Ltd.

Quinlan, J. R. (1986). Induction of decision trees. Machine Learning, 1, 81-106.

Quinlan, J. R. (1996). Bagging, Boosting and C4.5. Proceedings of 14th National Conference on Artificial Intelligence, 725–730.

Shafer, J., & Agrawal, R. (1996). A scalable parallel classifier for data mining. Proceedings of 1996 International Conference on Very Large Data Bases, 544–555.

Zhang, D., & Zhou, L. (2004). Discovering golden nuggets: data mining in financial application. In IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 4(4), 513-522.




DOI: http://dx.doi.org/10.12955/ejbe.v10i2.682

Refbacks

  • There are currently no refbacks.


Online ISSN 1804-9699

(c) 2018 CBU, o.p.s.