Applying Data Mining Techniques in a Real Problem

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Ghaffari, Bahere | 2009

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Type of Document: M.Sc. Thesis
Language: Farsi
Document No: 39517 (01)
University: Sharif University of Technology
Department: Industrial Engineering
Advisor(s): Salmasi, Naser
Abstract:
Data mining (DM) is one of the newest techniques which is used in decision making. DM helps the specialist to find the valuable knowledge which is hidden in data, with different techniques. DM has many research areas such as scientific research, medical fields, health care, fraud detection, marketing and customer relationship, sport, and games, and where ever there is data. Unfortunately, in our country, DM is not engaged seriously and there are fallacies of DM that weaken its efficiency. In this thesis, which is prepared in two sections, at the first section different techniques of DM are described and in the second section DM process is performed for a real world problem. For this purpose, a data set which contains about 100,000 records and 31 attributes for cell subscribers, is taken. Data mining process is performed according CRISP-DM standard and with Clementine software. The best models are created by k-means and C5.0 algorithms. K-means algorithm divided the data into five clusters which give us good idea about the data. C5.0 algorithm create most accurate model and tell us that the customers who use colored plans or who have a lot of sms, often don’t leave the company
Keywords:
Data Mining ; Clustering ; Classification ; Training Dataset ; Test Dataset

No TOC