document: Finding recent frequent itemsets adaptively over online 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 , specially for an online data stream , can provide valuable information for the analysis of the data stream . In addition , monitoring the continuous variation of a data stream enables to find the gradual change of embedded knowledge . However , most of mining algorithms over a data stream do not differentiate the information of recently generated transactions from the obsolete information of old transactions which may be no longer useful or possibly invalid at present . This paper proposes a data mining method for finding recent frequent itemsets adaptively over an online data stream . The effect of old transactions on the mining result of the 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 main memory usage . Finally , the proposed method is analyzed by a series of experiments .
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