Abstract

I want to thank Ethem Alpaydın for helping me all the time with ideas for my thesis and for his contribution to my undergraduate and graduate education. I want to thank Fikret Gürgen and Taner Bilgiç for their contribution to my undergraduate and graduate education and for participating in my thesis jury. I want to thank Dengiz Pınar, Nasuhi Sönmez and Ataman Kalkan of Gima Türk A.S¸. for supplying me the data I used in my thesis. I want to thank my family who always supported me and never left me alone during the preperation of this thesis. iv MARKET BASKET ANALYSIS FOR DATA MINING Most of the established companies have accumulated masses of data from their customers for decades. With the e-commerce applications growing rapidly, the companies will have a significant amount of data in months not in years. Data Mining, also known as Knowledge Discovery in Databases (KDD), is to find trends, patterns, correlations, anomalies in these databases which can help us to make accurate future decisions. Mining Association Rules is one of the main application areas of Data Mining. Given a set of customer transactions on items, the aim is to find correlations between the sales of items. We consider Association Mining in large database of customer transactions. We give an overview of the problem and explain approaches that have been used to attack this problem. We then give the description of the Apriori Algorithm and show results that are taken from Gima Türk A.S¸. a large Turkish supermarket chain. We also use two statistical methods: Principal Component Analysis and k-means to detect correlations between sets of items. v

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