Approximate mining of global closed frequent itemsets over data streams

摘要

This paper focuses on how to efficiently find the global Approximate Closed Frequent Itemsets (ACFIs) over streams. To achieve this purpose over a multiple, continuous, rapid and time-varying data stream, a fast, incremental, real-time and little-memory-cost algorithm should be regarded. Based on the max-frequency window model, a Max-Frequency Pattern Tree (MFP-Tree) structure is established to maintain summary information over the global stream. Subsequently, a novel algorithm Generating Global Approximate Closed Frequent Itemsets on Max-Frequency Window model (GGACFI-MFW) is proposed to update the MFP-Tree with high efficiency. The case studies show the efficiency and effectiveness of the proposed approach.