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 for a 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.

The mining problem over data streams has recently been attracting considerable attention thanks to the usefulness of data mining in various application fields of information science, and sequence data streams are so common in daily life. Therefore, a study on mining sequential patterns over sequence data streams can give valuable results for wide use in various application fields. This paper proposes a new framework for mining novel interesting sequential patterns over a sequence data stream and a mining method based on the framework. Assuming that a sequence with small time-intervals between its data elements is more valuable than others with large time-intervals, the novel interesting sequential pattern is defined and found by analyzing the time-intervals of data elements in a sequence as well as their orders. The proposed framework is capable of obtaining more interesting sequential patterns over sequence data streams whose data elements are highly correlated in terms of generation time.