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

Onur Doğan; Hakan  Aşan; Ejder Ayç

doi:10.12955/ejbe.v10i2.682

Onur Doğan Dokuz Eylül University, İzmir
Hakan Aşan Dokuz Eylül University, İzmir
Ejder Ayç Dokuz Eylül University, İzmir

DOI: https://doi.org/10.12955/ejbe.v10i2.682

Keywords: Decision making, Data mining, Decision tree algorithms

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.

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