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ONP-Miner: One-off Negative Sequential Pattern Mining

https://doi.org/10.1145/3549940
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Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-031-11217-1_3openalex-snapshot
Pattern Mining: Current Challenges and Opportunities
inferredcites →https://doi.org/10.1007/978-981-16-7566-9openalex-snapshot
Preference-based Spatial Co-location Pattern Mining
inferredcites →https://doi.org/10.1007/s10115-015-0884-xopenalex-snapshot
CloFAST: closed sequential pattern mining using sparse and vertical id-lists
inferredcites →https://doi.org/10.1007/s10115-017-1032-6openalex-snapshot
FCloSM, FGenSM: two efficient algorithms for mining frequent closed and generator sequences using the local pruning strategy
inferredcites →https://doi.org/10.1007/s10115-019-01333-6openalex-snapshot
Mining frequent and top-K High Utility Time Interval-based Events with Duration patterns
inferredcites →https://doi.org/10.1007/s10115-021-01614-zopenalex-snapshot
Generalized maximal utility for mining high average-utility itemsets
inferredcites →https://doi.org/10.1007/s10489-013-0499-4openalex-snapshot
Mining sequential patterns with periodic wildcard gaps
inferredcites →https://doi.org/10.1007/s10489-019-01492-7openalex-snapshot
Mining contrast sequential pattern based on subsequence time distribution variation with discreteness constraints
inferredcites →https://doi.org/10.1007/s10489-021-02763-yopenalex-snapshot
Self-adaptive nonoverlapping sequential pattern mining
inferredcites →https://doi.org/10.1007/s10489-021-02912-3openalex-snapshot
NetNMSP: Nonoverlapping maximal sequential pattern mining
inferredcites →https://doi.org/10.1007/s10489-021-03000-2openalex-snapshot
NetDPO: (delta, gamma)-approximate pattern matching with gap constraints under one-off condition
inferredcites →https://doi.org/10.1007/s11432-015-0935-3openalex-snapshot
Strict pattern matching under non-overlapping condition
inferredcites →https://doi.org/10.1016/j.artint.2016.03.001openalex-snapshot
e-NSP: Efficient negative sequential pattern mining
inferredcites →https://doi.org/10.1016/j.eswa.2018.12.046openalex-snapshot
Significance-based discriminative sequential pattern mining
inferredcites →https://doi.org/10.1016/j.eswa.2019.112967openalex-snapshot
Distributed mining of high utility time interval sequential patterns using mapreduce approach
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.future.2019.09.024openalex-snapshot
Efficient transaction deleting approach of pre-large based high utility pattern mining in dynamic databases
inferredcites →https://doi.org/10.1016/j.inffus.2021.05.011openalex-snapshot
Efficient closed high-utility pattern fusion model in large-scale databases
inferredcites →https://doi.org/10.1016/j.inffus.2021.10.017openalex-snapshot
A data-level fusion model for unsupervised attribute selection in multi-source homogeneous data
inferredcites →https://doi.org/10.1016/j.ins.2017.11.064openalex-snapshot
Mining maximal frequent patterns in transactional databases and dynamic data streams: A spark-based approach
inferredcites →https://doi.org/10.1016/j.ins.2018.04.013openalex-snapshot
Frequent pattern discovery with tri-partition alphabets
inferredcites →https://doi.org/10.1016/j.ins.2021.01.060openalex-snapshot
Efficient algorithms for mining frequent high utility sequences with constraints
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.ins.2021.12.064openalex-snapshot
NWP-Miner: Nonoverlapping weak-gap sequential pattern mining
inferredcites →https://doi.org/10.1016/j.knosys.2016.10.011openalex-snapshot
Efficient sequential pattern mining with wildcards for keyphrase extraction
inferredcites →https://doi.org/10.1016/j.knosys.2020.105812openalex-snapshot
NetNCSP: Nonoverlapping closed sequential pattern mining
inferredcites →https://doi.org/10.1016/j.knosys.2020.106596openalex-snapshot
Approximate high utility itemset mining in noisy environments
inferredcites →https://doi.org/10.1016/j.knosys.2021.107361openalex-snapshot
HANP-Miner: High average utility nonoverlapping sequential pattern mining
inferredcites →https://doi.org/10.1016/j.knosys.2022.109194openalex-snapshot
Negative pattern discovery with individual support
inferredcites →https://doi.org/10.1109/access.2018.2827167openalex-snapshot
Efficient High Utility Negative Sequential Patterns Mining in Smart Campus
inferredcites →https://doi.org/10.1109/tcbb.2020.2980234openalex-snapshot
Finding Possible Promoter Binding Sites in DNA Sequences by Sequential Patterns Mining With Specific Numbers of Gaps
inferredcites →https://doi.org/10.1109/tcyb.2017.2750691openalex-snapshot
NOSEP: Nonoverlapping Sequence Pattern Mining With Gap Constraints
inferredcites →https://doi.org/10.1109/tcyb.2018.2869907openalex-snapshot
e-RNSP: An Efficient Method for Mining Repetition Negative Sequential Patterns
inferredcites →https://doi.org/10.1109/tcyb.2021.3082114openalex-snapshot
Top- k Self-Adaptive Contrast Sequential Pattern Mining
inferredcites →https://doi.org/10.1109/tcyb.2022.3169327openalex-snapshot
OPP-Miner: Order-Preserving Sequential Pattern Mining for Time Series
inferredcites →https://doi.org/10.1109/tii.2019.2911064openalex-snapshot
Long-Term Monitoring for Leaks in Water Distribution Networks Using Association Rules Mining
inferredcites →https://doi.org/10.1109/tkde.2017.2759110openalex-snapshot
Redundancy Reduction for Prevalent Co-Location Patterns
inferredcites →https://doi.org/10.1109/tnnls.2018.2886199openalex-snapshot
Mining Top-${k}$ Useful Negative Sequential Patterns via Learning
inferredcites →https://doi.org/10.1109/tnnls.2020.3041732openalex-snapshot
Toward Better Structure and Constraint to Mine Negative Sequential Patterns
inferredcites →https://doi.org/10.1109/tnnls.2021.3063162openalex-snapshot
An Efficient Method for Modeling Nonoccurring Behaviors by Negative Sequential Patterns With Loose Constraints
inferredcites →https://doi.org/10.1109/tpds.2016.2637914openalex-snapshot
Distributed Mining of Contrast Patterns
inferredcites →https://doi.org/10.1145/3311952openalex-snapshot
Negative Sequence Analysis
inferredcites →https://doi.org/10.1145/3314107openalex-snapshot
A Survey of Parallel Sequential Pattern Mining
inferredcites →https://doi.org/10.1145/3397272openalex-snapshot
Contrast Pattern Mining in Paired Multivariate Time Series of a Controlled Driving Behavior Experiment
inferredcites →https://doi.org/10.1145/3399671openalex-snapshot
Efficient Mining of Outlying Sequence Patterns for Analyzing Outlierness of Sequence Data
inferredcites →https://doi.org/10.1145/3425498openalex-snapshot
Beyond Frequency
inferredcites →https://doi.org/10.1145/3430767openalex-snapshot
RHUPS
inferredcites →https://doi.org/10.1145/3440874openalex-snapshot
VM-NSP
inferredcites →https://doi.org/10.1145/3476247openalex-snapshot
OWSP-Miner: Self-adaptive One-off Weak-gap Strong Pattern Mining
inferredcites →https://doi.org/10.1145/3480245openalex-snapshot
NTP-Miner: Nonoverlapping Three-Way Sequential Pattern Mining
inferredcites →https://doi.org/10.1145/3495214openalex-snapshot
The Evolution of Search: Three Computing Paradigms

What points at this work

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

inferred← citeshttps://doi.org/10.1007/978-981-95-6786-7_35openalex-snapshot
From Burst to Routine: Mining Time-Aware Patterns from Sequential Dataset
inferred← citeshttps://doi.org/10.1007/s10489-024-06137-yopenalex-snapshot
A tree-based framework to mine top-K closed sequential patterns
inferred← citeshttps://doi.org/10.1007/s10618-025-01110-wopenalex-snapshot
Sequential pattern detection: similarities and differences across various fields
inferred← citeshttps://doi.org/10.1007/s41060-024-00659-xopenalex-snapshot
Sequential pattern mining algorithms and their applications: a technical review
inferred← citeshttps://doi.org/10.1016/j.asej.2026.104451openalex-snapshot
Temporal importance and utility based pattern analysis with negative units for industrial data streams
inferred← citeshttps://doi.org/10.1016/j.compbiolchem.2026.109186openalex-snapshot
Mining negative sequential patterns to improve viral genomic feature representation and classification
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.future.2025.107938openalex-snapshot
Mining one-off high average utility episodes for process event logs
inferred← citeshttps://doi.org/10.1016/j.ipm.2025.104077openalex-snapshot
TK-RNSP: Efficient Top- K Repetitive Negative 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.ipm.2026.104637openalex-snapshot
DS_HURNSP: An effective method for mining high utility repeated negative sequential patterns from data streams
inferred← citeshttps://doi.org/10.1016/j.ipm.2026.104643openalex-snapshot
TaNSP: An efficient target pattern mining algorithm based on negative sequential pattern
inferred← citeshttps://doi.org/10.1016/j.ipm.2026.104682openalex-snapshot
MNSPM: Merging-based nonoverlapping gap-constrained sequential pattern mining
inferred← citeshttps://doi.org/10.1016/j.knosys.2024.111449openalex-snapshot
SN-RNSP: Mining self-adaptive nonoverlapping repetitive negative sequential patterns in transaction sequences
inferred← citeshttps://doi.org/10.1016/j.knosys.2024.111885openalex-snapshot
Time-dependent frequent sequence mining-based survival analysis
inferred← citeshttps://doi.org/10.1016/j.knosys.2024.112398openalex-snapshot
Mining actionable repetitive positive and negative sequential patterns
inferred← citeshttps://doi.org/10.1016/j.knosys.2025.113161openalex-snapshot
Approximate erasable pattern discovery and analytics on stream data
inferred← citeshttps://doi.org/10.1016/j.knosys.2025.114803openalex-snapshot
CoTP-Miner: Co-occurrence three-way sequential pattern mining
inferred← citeshttps://doi.org/10.1016/j.neucom.2026.133529openalex-snapshot
A survey of sequence pattern mining: From precise data to uncertain data
inferred← citeshttps://doi.org/10.1109/bibm66473.2025.11356608openalex-snapshot
Leveraging Negative Sequential Patterns for Advanced Genomic Analysis and Classification
inferred← citeshttps://doi.org/10.1109/ickg55886.2022.00026openalex-snapshot
AOP-Miner: Approximate Order-Preserving Pattern Mining for Time Series
inferred← citeshttps://doi.org/10.1109/tetc.2025.3607892openalex-snapshot
OER-Miner: One-Off Episode Rule Mining for Process Event Logs
inferred← citeshttps://doi.org/10.1109/tfuzz.2026.3721254openalex-snapshot
FNP-Miner: Fuzzy Three-Way Nonoverlapping Sequential Pattern Mining
inferred← citeshttps://doi.org/10.1109/tkde.2022.3224963openalex-snapshot
OPR-Miner: Order-Preserving Rule Mining for Time Series
inferred← citeshttps://doi.org/10.1109/tkde.2023.3241213openalex-snapshot
MCoR-Miner: Maximal Co-Occurrence Nonoverlapping Sequential Rule Mining
inferred← citeshttps://doi.org/10.1109/tkde.2023.3321749openalex-snapshot
COPP-Miner: Top-k Contrast Order-Preserving Pattern Mining for Time Series Classification
inferred← citeshttps://doi.org/10.1109/tkde.2023.3334300openalex-snapshot
RNP-Miner: Repetitive Nonoverlapping Sequential Pattern Mining
inferred← citeshttps://doi.org/10.1109/tkde.2024.3438274openalex-snapshot
OPF-Miner: Order-Preserving Pattern Mining With Forgetting Mechanism for Time Series
inferred← citeshttps://doi.org/10.1109/tkde.2026.3680101openalex-snapshot
High-Utility Sequential Rule Mining Utilizing Segmentation Guided by Confidence
inferred← citeshttps://doi.org/10.1145/3658450openalex-snapshot
Co-occurrence Order-preserving Pattern Mining with Keypoint Alignment for Time Series
inferred← citeshttps://doi.org/10.1145/3706117openalex-snapshot
Unsupervised Outlier Detection with Reinforced Noise Discriminator
inferred← citeshttps://doi.org/10.1145/3716390openalex-snapshot
Mining Repetitive Negative Sequential Patterns with Gap Constraints
inferred← citeshttps://doi.org/10.1186/s40537-024-01056-8openalex-snapshot
Damped weighted erasable itemset mining with time sensitive dynamic environments
inferred← citeshttps://doi.org/10.3934/aci.2023004openalex-snapshot
From basic approaches to novel challenges and applications in Sequential Pattern Mining
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