A new data structure for finding maximum frequent itemset in online data mining

Frequent itemset mining is the first step of association rule mining. Association rule mining in online data mining is one of the most chalanging task due to data stream. A data stream is a huge, infinite, continuous, fast changing and rapid sequence of data elements. Traditional techniques for finding frequent itemset required many passes but stream data require only one scan over the data for finding frequent itemset so it is essential to use online algorithms for streaming data. This paper proposes an algorithm as well as a data structure for finding maximum frequent itemset in online data mining. A data structure consists of a tree which known as Ordered Tree. The structure of Ordered Tree such as it has 26 path if the item coded in alphabets, each path starts with a alphabetical letter and ends with the character Z. When we have more unique items then the coding can be with the numeric numbers. This Tree also known as multi path Tree, in which every node connected to their same neighbour node. Every transaction insert into Ordered Tree in sorted form and perform online frequent itemset mining. The proposed algorithm works online as well as offline. Experiment Result shows it as better algorithm for online and offline frequent itemset mining.