Efficient Tree Structures for High Utility Pattern Mining in Incremental Databases

TLDR

High‑utility pattern mining is crucial because it incorporates item frequencies and varying profits, and incremental mining leverages existing structures to reduce recomputation when databases or thresholds change. The study proposes three novel tree structures to enable efficient incremental and interactive high‑utility pattern mining. The authors introduce the IHUP‑L‑Tree, arranged lexicographically; the IHUP‑TF‑Tree, ordered by descending transaction frequency; and the IHUP‑TWU‑Tree, ordered by descending transaction‑weighted utilization to reduce mining time. The structures capture incremental data without restructuring, and extensive performance analyses show they are highly efficient and scalable for incremental and interactive HUP mining.

Abstract

Recently, high utility pattern (HUP) mining is one of the most important research issues in data mining due to its ability to consider the nonbinary frequency values of items in transactions and different profit values for every item. On the other hand, incremental and interactive data mining provide the ability to use previous data structures and mining results in order to reduce unnecessary calculations when a database is updated, or when the minimum threshold is changed. In this paper, we propose three novel tree structures to efficiently perform incremental and interactive HUP mining. The first tree structure, Incremental HUP Lexicographic Tree ({\rm IHUP}_{{\rm {L}}}-Tree), is arranged according to an item's lexicographic order. It can capture the incremental data without any restructuring operation. The second tree structure is the IHUP Transaction Frequency Tree ({\rm IHUP}_{{\rm {TF}}}-Tree), which obtains a compact size by arranging items according to their transaction frequency (descending order). To reduce the mining time, the third tree, IHUP-Transaction-Weighted Utilization Tree ({\rm IHUP}_{{\rm {TWU}}}-Tree) is designed based on the TWU value of items in descending order. Extensive performance analyses show that our tree structures are very efficient and scalable for incremental and interactive HUP mining.