Association rules finding with data mining

In this time period, many of companies and corporates specially store customer and sales data in databases together with technological developments. They want to obtain previously unknown, implicit, meaningful, and potentially useful information from data in databases with data mining techniques. Association rule mining is one kind of data mining techniques which discovers strong association or correlation relationships among a large of data items.The Apriori algorithm is the most popular association rule algorithm which discovers all frequent itemsets in large database of transactions. This algorithm uses iterative approach to count the frequent itemsets. Using this algorithm, candidate patterns which receive sufficient support from the database and the algorithm uses aprior gen actions join and prune to find all frequent itemsets.In this thesis, processes of knowledge discovery in databases, data mining, association rule and Apriori algorithm are explained.In the application, by using real data, market basket analysis application has performed by association rules and the results have been discussed. The aim of the study is to analyze knowledge discovery in databases, data mining and association rules, to carry out a market basket analysis by emphasizing on statistical analysis and to evaluate the results of the application.Key Words: Data Mining, Association Rules, Apriori Algorithm