%0 Journal Article
	%A Sara Ansari and  Mohammad Hadi Sadreddini
	%D 2009
	%J International Journal of Computer and Information Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 25, 2009
	%T An Efficient Approach to Mining Frequent Itemsets on Data Streams
	%U https://publications.waset.org/pdf/705
	%V 25
	%X The increasing importance of data stream arising in a
wide range of advanced applications has led to the extensive study of
mining frequent patterns. Mining data streams poses many new
challenges amongst which are the one-scan nature, the unbounded
memory requirement and the high arrival rate of data streams. In this
paper, we propose a new approach for mining itemsets on data
stream. Our approach SFIDS has been developed based on FIDS
algorithm. The main attempts were to keep some advantages of the
previous approach and resolve some of its drawbacks, and
consequently to improve run time and memory consumption. Our
approach has the following advantages: using a data structure similar
to lattice for keeping frequent itemsets, separating regions from each
other with deleting common nodes that results in a decrease in search
space, memory consumption and run time; and Finally, considering
CPU constraint, with increasing arrival rate of data that result in
overloading system, SFIDS automatically detect this situation and
discard some of unprocessing data. We guarantee that error of results
is bounded to user pre-specified threshold, based on a probability
technique. Final results show that SFIDS algorithm could attain
about 50% run time improvement than FIDS approach.
	%P 56 - 62