Decaying obsolete information in finding recent frequent itemsets over 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 likely to be changed as time goes by. However, most of mining algorithms or frequency approximation algorithms fora data stream are not able to extract the recent change of information in a data stream adaptively. This is because the obsolete information of old transactions which may be no longer useful or possibly invalid at present is regarded as important as that of recent transactions. This paper proposes an information decay method for finding recent frequent itemsets in a data stream. The effect of old transactions on the mining result of a data steam is gradually diminished as time goes by. Furthermore, the decay rate of information can be flexibly adjusted, which enables a user to define the desired life-time of the information of a transaction in a data stream.