Finding Frequent Items in Large Database by Using a New Dynamic Algorithm
Data mining is an efficient technology to discover patterns in large data base. Association rule mining is one of the most important techniques of data mining. Association rules are used to find the co-relation between the various item sets in a database. In recent years, the problem of finding association rules for large dataset has been proposed. There are many existing association algorithms to find frequent item sets. In this paper four algorithms are mentioned i.e. Apriori, Dynamic Item set Counting (DIC) algorithm, Similis algorithm and FP-growth algorithm.