Mining maximal frequent itemsets over a stream sliding window

Abstract

Maximal frequent itemsets are one of several condensed representations of frequent itemsets, which store most of the information contained in frequent itemsets using less space, thus being more suitable for stream mining. This paper considers a problem that how to mine maximal frequent itemsets over a stream sliding window. We employ a simple but effective data structure to dynamically maintain the maximal frequent itemsets and other helpful information; thus, an algorithm named MFIoSSW is proposed to efficiently mine the results in an incremental manner with our theoretical analysis. Our experimental results show our algorithm achieves a better running time cost.