2009 33rd Annual IEEE International Computer Software and Applications Conference (2009)
Seattle, Washington, USA
July 20, 2009 to July 24, 2009
An algorithm using a fading factor to detect the frequent data items in a stream is presented. Our algorithm can detect ε-approximate frequent data items on data stream using O(L+ε−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.
data mining, data stream, frequent items, time fading model
S. Zhang, L. Chen and L. Tu, "An Algorithm for Mining Frequent Items on Data Stream Using Fading Factor," 2009 33rd Annual IEEE International Computer Software and Applications Conference(COMPSAC), Seattle, Washington, USA, 2009, pp. 172-177.