# Utility Mining Across Multi-Dimensional Sequences

@article{Gan2021UtilityMA, title={Utility Mining Across Multi-Dimensional Sequences}, author={Wensheng Gan and Chun-Wei Lin and Jiexiong Zhang and Hongzhi Yin and Philippe Fournier-Viger and H. C. Chao and Philip S. Yu}, journal={ACM Transactions on Knowledge Discovery from Data (TKDD)}, year={2021}, volume={15}, pages={1 - 24} }

Knowledge extraction from database is the fundamental task in database and data mining community, which has been applied to a wide range of real-world applications and situations. Different from the support-based mining models, the utility-oriented mining framework integrates the utility theory to provide more informative and useful patterns. Time-dependent sequence data are commonly seen in real life. Sequence data have been widely utilized in many applications, such as analyzing sequential… Expand

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An Efficient Algorithm for Extracting High-Utility Hierarchical Sequential Patterns

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- Wirel. Commun. Mob. Comput.
- 2020

This paper incorporates the hierarchical relation of items into HUSPM and proposes a two-phase algorithm MHUH, the first algorithm for high-utility hierarchical sequential pattern mining (HUHSPM), which extracts more interesting patterns with underlying informative knowledge efficiently in HUH SPM. Expand

On-Shelf Utility Mining of Sequence Data

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- ACM Trans. Knowl. Discov. Data
- 2022

Two methods are proposed, OSUM of sequence data (OSUMS) and OSUMS+, to extract on-shelf high-utility sequential patterns and substantial experimental results show that the two methods outperform the state-of-the-art algorithm. Expand

TUSQ: Targeted High-Utility Sequence Querying

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- ArXiv
- 2021

A novel algorithm, namely targeted high-utility sequence querying (TUSQ), based on two novel upper bounds suffix remain utility and terminated descendants utility as well as a vertical Last Instance Table structure is developed. Expand

Explainable Fuzzy Utility Mining on Sequences

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- ArXiv
- 2021

This study investigates explainable fuzzy-theoretic utility mining on multi-sequences and proposes a novel method termed pattern growth fuzzy utility mining (PGFUM), which achieves not only human-explainable mining results that contain the original nature of revealable intelligibility, but also high efficiency in terms of runtime and memory cost. Expand

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