Efficient Mining of Temporal High Utility Itemsets from Data streams

Abstract

itemsets are considered as the different values of individual items as utilities, and utility mining aims at identifying the itemsets with high utilities. The temporal high utility itemsets are the itemsets with support larger than a pre-specified threshold in current time window of data stream. Discovery of temporal high utility itemsets is an important process for mining interesting patterns like association rules from data streams. In this paper, we propose a novel method, namely THUI (Temporal High Utility Itemsets) -Mine, for mining temporal high utility itemsets from data streams efficiently and effectively. To our best knowledge, this is the first work on mining temporal high utility itemsets from data streams. The novel contribution of THUI-Mine is that it can effectively identify the temporal high utility itemsets by generating fewer temporal high transaction-weighted utilization 2-itemsets such that the execution time can be reduced substantially in mining all high utility itemsets in data streams. In this way, the process of discovering all temporal high utility itemsets under all time windows of data streams can be achieved effectively with limited memory space, less candidate itemsets and CPU I/O time. This meets the critical requirements on time and space efficiency for mining data streams. The experimental results show that THUI-Mine can discover the temporal high utility itemsets with higher performance and less candidate itemsets compared to other algorithms under various experimental conditions.