Market basket analysis for data mining

Abstract

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 amout of data in months not in years. Data Mining, also known as Knowledge Dicovery 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 to sales of items. We consider Association Mining in large database of customer transactions. We give an overview of the problem and explain aooroaches 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.Ş. a large Turkish supermarket chain. We also use two statistical methods: Principal Component Analysis and k-means to detect correlations between sets of items.