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.
[1]
G. V. Kass.
An Exploratory Technique for Investigating Large Quantities of Categorical Data
,
1980
.
[2]
Philip J. Stone,et al.
Experiments in induction
,
1966
.
[3]
Dongsong Zhang,et al.
Discovering golden nuggets: data mining in financial application
,
2004,
IEEE Trans. Syst. Man Cybern. Part C.
[4]
Jorma Rissanen,et al.
SLIQ: A Fast Scalable Classifier for Data Mining
,
1996,
EDBT.
[5]
Sulin Pang,et al.
C5.0 Classification Algorithm and Application on Individual Credit Evaluation of Banks
,
2009
.
[6]
R. Suganya,et al.
Data Mining Concepts and Techniques
,
2010
.
[7]
Gregory Piatetsky-Shapiro,et al.
Knowledge Discovery in Databases: An Overview
,
1992,
AI Mag..
[8]
R. R.Kabra,et al.
Performance Prediction of Engineering Students using Decision Trees
,
2011
.
[9]
J. Ross Quinlan,et al.
Bagging, Boosting, and C4.5
,
1996,
AAAI/IAAI, Vol. 1.
[10]
Catherine Bounsaythip,et al.
Overview of Data Mining for Customer Behavior Modeling
,
2001
.
[11]
Michael J. A. Berry,et al.
Data mining techniques - for marketing, sales, and customer support
,
1997,
Wiley computer publishing.
[12]
R. S. Bichkar,et al.
Performance Prediction of Engineering Students using Decision Trees
,
2011
.
[13]
Rakesh Agrawal,et al.
SPRINT: A Scalable Parallel Classifier for Data Mining
,
1996,
VLDB.
[14]
J. Ross Quinlan,et al.
Induction of Decision Trees
,
1986,
Machine Learning.
[15]
Heikki Mannila,et al.
Principles of Data Mining
,
2001,
Undergraduate Topics in Computer Science.
[16]
Yoav Freund,et al.
A decision-theoretic generalization of on-line learning and an application to boosting
,
1995,
EuroCOLT.