An Efficient Method for Mining Temporal Emerging Itemsets in Data Streams

Abstract

In this paper, we propose a new method, namely EFI (Emerging Frequent Itemset)-Mine, for mining temporal emerging frequent itemsets from data streams efficiently and effectively. Discovery of emerging frequent itemsets is an important process for mining interesting patterns from data streams. The novel contribution of EFI-Mine is that it can effectively identify the potential emerging itemsets such that the execution time can be reduced substantially in mining all frequent itemsets in data streams. This meets the critical requirements of time and space efficiency for mining data streams. The experimental results show that EFI-Mine can find the emerging frequent itemsets with high precision under different experimental conditions and it performs scalable in terms of execution time.