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.
recent frequent itemsets frequent itemsets online data stream data sequence continuation knowledge embedding change timing informal analysis monitor variation mining algorithm transaction paper data mining method effect optimality process memorialized experience decay mechanism pruning of itemsets delayed-insertion