An Algorithm for Mining Frequent Items on Data Stream Using Fading Factor

An algorithm using a fading factor to detect the frequent data items in a stream is presented. Our algorithm can detect epsiv-approximate frequent data items on data stream using O(L+epsiv^-1) memory space where L is a constant, and the processing time for each data item is O(1). Experimental results on several artificial datasets and real datasets show our algorithm has higher precision, requires less memory and computation time than other similar methods.