Frequent Items Mining on Data Stream Based on Time Fading Factor
Shan Zhang, Ling Chen, Li Tu · 2009
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 ¿-approximate frequent data items on data stream using O(¿-1) memory space and the processing time for each data item and a query is O(¿-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.