Finding recently frequent itemsets adaptively over online transactional data streams

A data stream is a massive unbounded sequence of data elements continuously generated at a rapid rate. Consequently, the knowledge embedded in a data stream is more likely to be changed as time goes by. Identifying the recent change of a data stream, especially for an online data stream, can provide valuable information for the analysis of the data stream. However, most of mining algorithms or frequency approximation algorithms over a data stream do not differentiate the information of recently generated data elements from the obsolete information of old data elements which may be no longer useful or possibly invalid at present. Therefore, they are not able to extract the recent change of information in a data stream adaptively. This paper proposes a data mining method for finding recently frequent itemsets adaptively over an online transactional data stream. The effect of old transactions on the current mining result of a data steam is diminished by decaying the old occurrences of each itemset as time goes by. Furthermore, several optimization techniques are devised to minimize processing time as well as memory usage. Finally, the performance of the proposed method is analyzed by a series of experiments to identify its various characteristics.