What does this research mean for the field?

The Rapid mining High Utility Sequential Pattern with Negative items (RHUSN) algorithm significantly outperforms existing methods in runtime, memory consumption, and candidate pattern reduction by utilizing a compact data structure and a tighter utility upper bound. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

To develop an efficient algorithm for high utility sequential pattern mining that addresses high computational and memory costs caused by the coexistence of positive and negative item values.

September 8, 2026Statistical Analysis and Data Mining The ASA Data Science Journal

Rapid High Utility Sequential Pattern Mining With Negative and Positive Values

View Full Paper

Ask AI

Bookmark

Authors

MHMeng HanQingdao University YLYikai LiNorth Minzu University CMCuicui MaNorth Minzu University

Discussion

Member takes

Implication

Computational study demonstrates improved speed and memory efficiency in sequential pattern mining with negative utilities, highlighting scalable transaction analysis.

Key Points

To develop an efficient algorithm for high utility sequential pattern mining that addresses high computational and memory costs caused by the coexistence of positive and negative item values.
Designed the RHUSN algorithm incorporating the Neg-Seq-Pro data structure, which uses shared negative sequence arrays and explicit projection lists for unified utility modeling.
Introduced a tighter utility upper bound (NTRSU) paired with a two-tier pruning framework consisting of prefix-level and item-level pruning.
Evaluated the proposed approach against existing representative methods across runtime, memory usage, and candidate pattern generation.
RHUSN significantly outperformed representative baseline methods in overall execution time and memory consumption.
The NTRSU upper bound and two-tier pruning strategy markedly curtailed candidate pattern expansion and eliminated redundant calculations without compromising result completeness.

Cite This Study

Han et al. (2026) studied this question.

synapsesocial.com/papers/6a9fd7ab58e84d0ff5b46884 https://doi.org/10.1002/sam.70107

View Full Paper

Ask AI

Bookmark

Implication

Computational study demonstrates improved speed and memory efficiency in sequential pattern mining with negative utilities, highlighting scalable transaction analysis.

Rapid High Utility Sequential Pattern Mining With Negative and Positive Values

Discussion

Member takes

Implication

Key Points

Cite This Study

Rapid High Utility Sequential Pattern Mining With Negative and Positive Values

Discussion

Member takes

Implication

Key Points

Cite This Study

Also Consider

Also Consider