Mining Maximum Frequent Item Sets Over Data Streams Using Transaction Sliding Window Techniques

As we know that the online mining of streaming data is one of the most important issues in data mining. In this paper, we proposed an efficient one.frequent item sets over a transaction-sensitive sliding window), to mine the set of all frequent item sets in data streams with a transaction-sensitive sliding window. An effective bit-sequence representation of items is used in the proposed algorithm to reduce the time and memory needed to slide the windows. The experiments show that the proposed algorithm not only attain highly accurate mining results, but also the performance significant faster and consume less memory than existing algorithms for mining frequent item sets over recent data streams. In this paper our theoretical analysis and experimental studies show that the proposed algorithm is efficient and scalable and perform better for mining the set of all maximum frequent item sets over the entire history of the data streams.