Mining frequent items in data stream using time fading model
Information Sciences: an International Journal
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Most of the existing algorithms for mining frequent items on data stream do not emphasis the importance of the recent data items. We present an algorithm using a fading factor to detect the data items with frequency counts exceeding a user-specified threshold. Our algorithm can detect ε-approximate frequent data items on data stream using O(ε-1) memory space and the processing time for each data item and a query is O(ε-1). Experimental results on several artificial datasets and real datasets show our algorithm has higher precision, requires less memory and consumes less computation time than other similar methods.