A Fast and Efficient Algorithm for Finding Frequent Items over Data Stream

We investigate the problem of finding the frequent items in a continuous data stream. We present an algorithm called λ-Count for computing frequency counts over a user specified threshold on a data stream. To emphasize the importance of the more recent data items, a fading factor  is used. Our algorithm can detect εapproximate frequent items of a data stream using O(logλε) memory space and O(1) time to process each data record. The computation time for answering each query is O( lo g   ), and for answering a query about the frequentness of a given data item is O(1). Experimental study shows that λ-Count outperforms other methods in terms of accuracy, memory requirement, and processing speed.