Research On The Algorithm Of Data Stream Frequent Itemsets Mining

A growing number of emerging applications have to handle various data streams. Some interesting results have been reported for techniques and algorithms of handling data streams, and some stream projects have been proposed. A data stream is an ordered sequence of items that arrives in timely order. Different from data in traditional static databases, data streams are continuous, unbounded, usually come with high speed and have a data distribution that often changes with time. Frequent pattern mining is one of the fundamental problems in data stream mining. It has received considerable attention in the past few years. Some effective algorithms have been proposed.According to the characteristics of data stream, the paper researches and summarizes the technique of data steam processing, issues in data stream mining. The paper researches some techniques of solving the issues. The paper introduces some mining algorithms of classical frequent itemsets and does some experiments. Through analysis, it is difficult to make classical frequent itemsets mining algorithms to extend to data stream because of the limitless and high speed of data stream. The paper introduces, analyzes and summarizes some existent data stream mining algorithmsr. Besides, the paper designs and realizes FP-stream and Time-Sensitive Sliding Window algorithms of data stream frequent itemsets mining.On the basis of the above work, the paper proposes FP-FT algorithm.The algorithm stores frequent patterns based on prefix tree with time windows which can save some space. The analysis and experiment results show that this algorithm has a good performance in speed and space.