Mining frequent itemsets over data streams using efficient window sliding techniques

Abstract

Online mining of frequent itemsets over it stream sliding window is one of the most important problems in stream data mining with broad applications. It is also a difficult issue since the streaming data possess some challenging characteristics, such as unknown or unbound size, possibly a very fast arrival rate, inability to backtrack over previously arrived transactions, and a lack of system control over the order in which the data arrive. In this paper, we propose an effective bit-sequence based, one-pass algorithm, called MFI-TransSW (Mining Frequent/temsets within a Transaction-sensitive Sliding Window), to mine the set of frequent itemsets from data streams within a transaction-sensitive sliding window which consists of a fixed number of transactions. The proposed MFI-TransSW algorithm consists of three phases: window initialization, window sliding and pattern generation. First, every item of each transaction is encoded in ail effective bit-sequence representation in the window initialization phase. The proposed bit-sequence representation of item is used to reduce the time and memory needed to slide the windows in the following phases. Second, MFI-TransSW uses the left bit-shift technique to slide the windows efficiently in the window sliding phase. Finally, the complete set of frequent itemsets within the current sliding window is generated by it level-wise method in the pattern generation phase. Experimental studies show that the proposed algorithm not only attain highly accurate mining results, but also run significant faster and consume less memory than do existing algorithms for mining frequent itemsets over data streams with a sliding window. Furthermore, based oil the MFI-TransSW framework, ail extended single-pass algorithm, called MFI-TimeSW (Mining Frequent/temsets within a Time-sensitive Sliding Window) is presented to mine the set of frequent itemsets efficiently over time-sensitive sliding windows. (c) 2007 Elsevier Ltd. All rights reserved.