Efficient algorithms for incremental utility mining

TLDR

Temporal data mining seeks patterns in large temporal datasets, while utility mining identifies high‑utility itemsets, and Tseng et al. introduced temporal utility mining in 2006 by extending both concepts. This study investigates incremental utility mining to identify all high‑temporal‑utility itemsets within a specified time period on an incremental transaction database. The authors propose two efficient algorithms, Incremental Utility Mining (IUM) and Fast Incremental Utility Mining (FIUM), to perform this identification. Experimental results demonstrate that both IUM and FIUM are efficient.

Abstract

Temporal data mining is the activity of finding interesting correlations or patterns in large temporal data sets. On the other hand, utility mining aims at identifying the itemsets with high utilities. In 2006, Tseng et al. introduced the temporal utility mining which is extended from both temporal association rule mining and utility mining. In this study, we investigated the incremental utility mining which can identify all high temporal utility itemsets in a specified time period on an incremental transaction database. Two efficient algorithms, Incremental Utility Mining (IUM) and Fast Incremental Utility Mining (FIUM), were proposed. The experimental results also showed that both algorithms are efficient.