Frequent Itemset Mining in Transactional Data Streams Based on Quality Control and Resource Adaptation

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.Further the usage of memory resources should be taken care of regardless of the amount of data generated in the stream. In this work we extend the ideas of existing proposals to ensure efficient resource utilization and quality control. The proposed algorithm RAQ-FIG (Resource Adaptive Quality Assuring Frequent Item Generation) accounts for the computational resources like memory available and dynamically adapts the rate of processing based on the available memory. It will compute the recent approximate frequent itemsets by using a single pass algorithm. The empirical results demonstrate the efficacy of the proposed approach for finding recent frequent itemsets from a data stream.