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HUSP-SP: Faster Utility Mining on Sequence Data

https://doi.org/10.1145/3597935
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/widm.1242openalex-snapshot
A survey of incremental high‐utility itemset mining
inferredcites →https://doi.org/10.1007/11430919_79openalex-snapshot
A Two-Phase Algorithm for Fast Discovery of High Utility Itemsets
inferredcites →https://doi.org/10.1007/978-3-030-04921-8_4openalex-snapshot
A Survey of High Utility Sequential Pattern Mining
inferredcites →https://doi.org/10.1007/s10115-015-0914-8openalex-snapshot
On efficiently mining high utility sequential patterns
inferredcites →https://doi.org/10.1007/s10115-018-1161-6openalex-snapshot
Mining sequential patterns with itemset constraints
inferredcites →https://doi.org/10.1007/s10994-016-5617-1openalex-snapshot
Memory-adaptive high utility sequential pattern mining over data streams
inferredcites →https://doi.org/10.1007/s11704-016-6245-4openalex-snapshot
CLS-Miner: efficient and effective closed high-utility itemset mining
inferredcites →https://doi.org/10.1016/j.engappai.2019.05.010openalex-snapshot
FMaxCloHUSM: An efficient algorithm for mining frequent closed and maximal high utility sequences
inferredcites →https://doi.org/10.1016/j.eswa.2012.05.035openalex-snapshot
Efficient algorithms for mining maximal high utility itemsets from data streams with different models
inferredcites →https://doi.org/10.1016/j.eswa.2021.115449openalex-snapshot
HAOP-Miner: Self-adaptive high-average utility one-off sequential pattern mining
inferredcites →https://doi.org/10.1016/j.ins.2019.07.005openalex-snapshot
Correlated utility-based pattern mining
inferredcites →https://doi.org/10.1016/j.ins.2019.10.033openalex-snapshot
ProUM: Projection-based utility mining on sequence data
inferredcites →https://doi.org/10.1016/j.ins.2021.04.035openalex-snapshot
TKUS: Mining top-k high utility sequential patterns
inferredcites →https://doi.org/10.1016/j.knosys.2015.12.019openalex-snapshot
Efficient algorithms for mining high-utility itemsets in uncertain databases
inferredcites →https://doi.org/10.1023/b:dami.0000005258.31418.83openalex-snapshot
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
inferredcites →https://doi.org/10.1109/69.553155openalex-snapshot
Data mining: an overview from a database perspective
inferredcites →https://doi.org/10.1109/bigdata.2018.8622405openalex-snapshot
Privacy Preserving Utility Mining: A Survey
inferredcites →https://doi.org/10.1109/bigdata47090.2019.9006152openalex-snapshot
Utility-Driven Mining of High Utility Episodes
inferredcites →https://doi.org/10.1109/icde.2001.914830openalex-snapshot
PrefixSpan,: mining sequential patterns efficiently by prefix-projected pattern growth
inferredcites →https://doi.org/10.1109/icdm.2003.1250893openalex-snapshot
Mining high utility itemsets
inferredcites →https://doi.org/10.1109/jiot.2020.3026826openalex-snapshot
Large-Scale High-Utility Sequential Pattern Analytics in Internet of Things
inferredcites →https://doi.org/10.1109/snpd.2010.21openalex-snapshot
Mining High Utility Web Access Sequences in Dynamic Web Log Data
inferredcites →https://doi.org/10.1109/tbdata.2022.3175428openalex-snapshot
TUSQ: Targeted High-Utility Sequence Querying
inferredcites →https://doi.org/10.1109/tcyb.2020.2970176openalex-snapshot
Fast Utility Mining on Sequence Data
inferredcites →https://doi.org/10.1109/tfuzz.2021.3089284openalex-snapshot
Explainable Fuzzy Utility Mining on Sequences
inferredcites →https://doi.org/10.1109/tkde.2009.46openalex-snapshot
Efficient Tree Structures for High Utility Pattern Mining in Incremental Databases
inferredcites →https://doi.org/10.1109/tkde.2012.59openalex-snapshot
Efficient Algorithms for Mining High Utility Itemsets from Transactional Databases
inferredcites →https://doi.org/10.1109/tkde.2015.2420557openalex-snapshot
CRoM and HuspExt: Improving Efficiency of High Utility Sequential Pattern Extraction
inferredcites →https://doi.org/10.1109/tkde.2015.2458860openalex-snapshot
Efficient Algorithms for Mining Top-K High Utility Itemsets
inferredcites →https://doi.org/10.1109/tkde.2019.2942594openalex-snapshot
A Survey of Utility-Oriented Pattern Mining
inferredcites →https://doi.org/10.1142/s0218001417500355openalex-snapshot
Mining High Utility Sequential Patterns with Negative Item Values
inferredcites →https://doi.org/10.1145/1148170.1148215openalex-snapshot
Identifying comparative sentences in text documents
inferredcites →https://doi.org/10.1145/1835804.1835839openalex-snapshot
UP-Growth
inferredcites →https://doi.org/10.1145/191839.191972openalex-snapshot
Quest
inferredcites →https://doi.org/10.1145/2339530.2339636openalex-snapshot
USpan
inferredcites →https://doi.org/10.1145/2396761.2396773openalex-snapshot
Mining high utility itemsets without candidate generation
inferredcites →https://doi.org/10.1145/253260.253325openalex-snapshot
Dynamic itemset counting and implication rules for market basket data
inferredcites →https://doi.org/10.1145/299432.299445openalex-snapshot
Modeling protein families using probabilistic suffix trees
inferredcites →https://doi.org/10.1145/3178114openalex-snapshot
On Incremental High Utility Sequential Pattern Mining
inferredcites →https://doi.org/10.1145/3314107openalex-snapshot
A Survey of Parallel Sequential Pattern Mining
inferredcites →https://doi.org/10.1145/3362070openalex-snapshot
Utility Mining across Multi-Sequences with Individualized Thresholds
inferredcites →https://doi.org/10.1145/3446938openalex-snapshot
Utility Mining Across Multi-Dimensional Sequences
inferredcites →https://doi.org/10.1145/3457570openalex-snapshot
On-Shelf Utility Mining of Sequence Data
inferredcites →https://doi.org/10.4218/etrij.10.1510.0066openalex-snapshot
A Novel Approach for Mining High-Utility Sequential Patterns in Sequence Databases

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1002/sam.70107openalex-snapshot
Rapid High Utility Sequential Pattern Mining With Negative and Positive Values
inferred← citeshttps://doi.org/10.1007/s10044-025-01505-xopenalex-snapshot
NEONATE: a bi-directional search strategy for non-overlapping pattern matching with gap constraint
inferred← citeshttps://doi.org/10.1007/s11227-024-06275-7openalex-snapshot
TKU-BChOA: an accurate meta-heuristic method to mine Top-k high utility itemsets
inferred← citeshttps://doi.org/10.1016/j.engappai.2024.109742openalex-snapshot
U-HPAUSM: Mining high probability average utility sequences in uncertain quantitative sequential databases
inferred← citeshttps://doi.org/10.1016/j.eswa.2024.125955openalex-snapshot
Enabling knowledge discovery through low utility itemset mining
inferred← citeshttps://doi.org/10.1016/j.eswa.2025.126536openalex-snapshot
HUPSP-LAL: Efficiently mining utility-driven sequential patterns in uncertain sequences
inferred← citeshttps://doi.org/10.1016/j.eswa.2025.127836openalex-snapshot
Temporal fuzzy utility-based data analysis on data streams
inferred← citeshttps://doi.org/10.1016/j.eswa.2026.132429openalex-snapshot
SNA-SPM: A sequence nodelist based algorithm for sequential pattern mining
inferred← citeshttps://doi.org/10.1016/j.ipm.2025.104402openalex-snapshot
HU-RNSP: Efficiently mining high-utility repeated negative sequential patterns
inferred← citeshttps://doi.org/10.1016/j.knosys.2024.111796openalex-snapshot
Mining Interesting Sequential Patterns using a Novel Balanced Utility Measure
inferred← citeshttps://doi.org/10.1016/j.knosys.2025.114185openalex-snapshot
Skyline quantity-utility sequential pattern mining: An efficient and effective approach
inferred← citeshttps://doi.org/10.1016/j.knosys.2026.115314openalex-snapshot
MEU-miner: Bitset-guided multi-dimensional high-utility sequential pattern mining with parallelism and adaptive top-k
inferred← citeshttps://doi.org/10.1109/aic66080.2025.11212206openalex-snapshot
UTIL-DFS: Utility-Based Sequential Rule Mining for Identifying Fraudulent Transactions
inferred← citeshttps://doi.org/10.1109/ic2sda68097.2025.11331408openalex-snapshot
On Efficiently Mining Top-K High Utility Sequential Patterns
inferred← citeshttps://doi.org/10.1109/icftic68075.2025.11324988openalex-snapshot
Multi-dimensional High Utility Sequential Mining: An Efficient and Concrete-Oriented Algorithm
inferred← citeshttps://doi.org/10.1109/icpads60453.2023.00081openalex-snapshot
Fast RFM Analysis in Sequence Data
inferred← citeshttps://doi.org/10.1109/tkde.2026.3680101openalex-snapshot
High-Utility Sequential Rule Mining Utilizing Segmentation Guided by Confidence
inferred← citeshttps://doi.org/10.1177/1088467x241301387openalex-snapshot
An efficient algorithm for high utility sequential pattern mining over data streams based on sliding window model
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