Mining Frequent Itemsets from Online Data Streams: Comparative Study

Abstract

—Online mining of data streams poses many new challenges more than mining static databases. In addition to the one-scan nature, the unbounded memory requirement, the high data arrival rate of data streams and the combinatorial explosion of itemsets exacerbate the mining task. The high complexity of the frequent itemsets mining problem hinders the application of the stream mining techniques. In this review, we present a comparative study among almost all, as we are acquainted, the algorithms for mining frequent itemsets from online data streams. All those techniques immolate with the accuracy of the results due to the relatively limited storage, leading, at all times, to approximated results.