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NTP-Miner: Nonoverlapping Three-Way Sequential Pattern Mining

https://doi.org/10.1145/3480245
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Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-030-04921-8_1openalex-snapshot
A Survey of High Utility Itemset Mining
inferredcites →https://doi.org/10.1007/978-3-319-31753-3_29openalex-snapshot
OCEAN: Fast Discovery of High Utility Occupancy Itemsets
inferredcites →https://doi.org/10.1007/978-3-319-39937-9_7openalex-snapshot
Mining Top-k Distinguishing Sequential Patterns with Flexible Gap Constraints
inferredcites →https://doi.org/10.1007/s10115-019-01385-8openalex-snapshot
High average-utility sequential pattern mining based on uncertain databases
inferredcites →https://doi.org/10.1007/s10489-013-0443-7openalex-snapshot
Mining high utility itemsets by dynamically pruning the tree structure
inferredcites →https://doi.org/10.1007/s10489-013-0499-4openalex-snapshot
Mining sequential patterns with periodic wildcard gaps
inferredcites →https://doi.org/10.1007/s10489-019-01616-zopenalex-snapshot
NetNPG: Nonoverlapping pattern matching with general gap constraints
inferredcites →https://doi.org/10.1007/s11390-014-1464-3openalex-snapshot
Pattern Matching with Flexible Wildcards
inferredcites →https://doi.org/10.1007/s11432-015-0935-3openalex-snapshot
Strict pattern matching under non-overlapping condition
inferredcites →https://doi.org/10.1016/0020-0190(91)90032-dopenalex-snapshot
An algorithm for string matching with a sequence of don't cares
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.2019.113087openalex-snapshot
Efficient approach for incremental weighted erasable pattern mining with list structure
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.2009.09.021openalex-snapshot
Three-way decisions with probabilistic rough sets
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.2018.11.026openalex-snapshot
Mining weighted frequent sequences in uncertain databases
inferredcites →https://doi.org/10.1016/j.ins.2019.04.050openalex-snapshot
Advanced approach of sliding window based erasable pattern mining with list structure of industrial fields
inferredcites →https://doi.org/10.1016/j.ins.2019.06.003openalex-snapshot
Granularity-driven sequential three-way decisions: A cost-sensitive approach to classification
inferredcites →https://doi.org/10.1016/j.ins.2020.09.044openalex-snapshot
Mining local periodic patterns in a discrete sequence
inferredcites →https://doi.org/10.1016/j.ins.2021.07.091openalex-snapshot
TWD-SFNN: Three-way decisions with a single hidden layer feedforward neural network
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.2019.105241openalex-snapshot
Mining cost-effective patterns in event logs
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.2021.107361openalex-snapshot
HANP-Miner: High average utility nonoverlapping sequential pattern mining
inferredcites →https://doi.org/10.1088/1742-5468/2005/09/p09008openalex-snapshot
Comparing community structure identification
inferredcites →https://doi.org/10.1093/jigpal/jzz068openalex-snapshot
Mining correlated high-utility itemsets using various measures
inferredcites →https://doi.org/10.1109/access.2016.2593953openalex-snapshot
Discovering Patterns With Weak-Wildcard Gaps
inferredcites →https://doi.org/10.1109/hicss.2014.231openalex-snapshot
Word Cloud Explorer: Text Analytics Based on Word Clouds
inferredcites →https://doi.org/10.1109/icde.2009.104openalex-snapshot
Efficient Mining of Closed Repetitive Gapped Subsequences from a Sequence Database
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.2019.2896267openalex-snapshot
HUOPM: High-Utility Occupancy Pattern Mining
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.2022.3149158openalex-snapshot
IEEE Transactions on Cybernetics
inferredcites →https://doi.org/10.1109/tfuzz.2020.2964376openalex-snapshot
IEEE Transactions on Fuzzy Systems
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.2017.2759110openalex-snapshot
Redundancy Reduction for Prevalent Co-Location Patterns
inferredcites →https://doi.org/10.1109/tkde.2018.2833478openalex-snapshot
Efficient Vertical Mining of High Average-Utility Itemsets Based on Novel Upper-Bounds
inferredcites →https://doi.org/10.1109/tkde.2019.2897311openalex-snapshot
Mining Behavioral Sequence Constraints for Classification
inferredcites →https://doi.org/10.1109/tkde.2020.3002926openalex-snapshot
An efficient Split-Merge re-start for the K-means algorithm
inferredcites →https://doi.org/10.1109/tnnls.2018.2886199openalex-snapshot
Mining Top-${k}$ Useful Negative Sequential Patterns via Learning
inferredcites →https://doi.org/10.1145/2133360.2133362openalex-snapshot
Efficient Mining of Gap-Constrained Subsequences and Its Various Applications
inferredcites →https://doi.org/10.1145/2700409openalex-snapshot
Classification with Streaming Features: An Emerging-Pattern Mining Approach
inferredcites →https://doi.org/10.1145/2753765openalex-snapshot
Occupancy-Based Frequent Pattern Mining *
inferredcites →https://doi.org/10.1145/3281632openalex-snapshot
PSP-AMS
inferredcites →https://doi.org/10.1145/3314107openalex-snapshot
A Survey of Parallel Sequential Pattern Mining
inferredcites →https://doi.org/10.1145/3399671openalex-snapshot
Efficient Mining of Outlying Sequence Patterns for Analyzing Outlierness of Sequence Data
inferredcites →https://doi.org/10.1145/3476247openalex-snapshot
OWSP-Miner: Self-adaptive One-off Weak-gap Strong Pattern Mining
inferredcites →https://doi.org/10.23940/ijpe.20.04.p6.549559openalex-snapshot
Knowledge Point Recommendation Algorithm based on Enhanced Correction Factor and Weighted Sequential Pattern Mining
inferredcites →https://doi.org/10.3233/ida-163231openalex-snapshot
Mining multi-relational high utility itemsets from star schemas

What points at this work

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

inferred← citeshttps://doi.org/10.1002/cpe.7265openalex-snapshot
Data attribute oriented business process effective infrequency behavior mining method
inferred← citeshttps://doi.org/10.1007/978-3-031-96157-1_46openalex-snapshot
A New Way to Group and Align Process Traces in Real-World
inferred← citeshttps://doi.org/10.1007/978-3-662-66146-8_6openalex-snapshot
A Pattern Mining Framework for Improving Billboard Advertising Revenue
inferred← citeshttps://doi.org/10.1007/s10489-021-02763-yopenalex-snapshot
Self-adaptive nonoverlapping sequential pattern mining
inferred← citeshttps://doi.org/10.1007/s10489-021-02912-3openalex-snapshot
NetNMSP: Nonoverlapping maximal sequential pattern mining
inferred← citeshttps://doi.org/10.1007/s10489-021-03000-2openalex-snapshot
NetDPO: (delta, gamma)-approximate pattern matching with gap constraints under one-off condition
inferred← citeshttps://doi.org/10.1007/s11042-023-14484-0openalex-snapshot
Mining frequent Itemsets from transaction databases using hybrid switching framework
inferred← citeshttps://doi.org/10.1016/j.artmed.2022.102406openalex-snapshot
Septic shock prediction and knowledge discovery through temporal pattern mining
inferred← citeshttps://doi.org/10.1016/j.eswa.2023.121738openalex-snapshot
An efficient pruning method for mining inter-sequence patterns based on pseudo-IDList
inferred← citeshttps://doi.org/10.1016/j.eswa.2023.122754openalex-snapshot
A new tree-based approach to mine sequential patterns
inferred← citeshttps://doi.org/10.1016/j.eswa.2024.123414openalex-snapshot
An approach based on maximal cliques and multi-density clustering for regional co-location pattern mining
inferred← citeshttps://doi.org/10.1016/j.eswa.2026.133884openalex-snapshot
BHAN-Miner: Bitmap-based top-k high average-utility non-overlapping sequential pattern mining
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.ins.2023.03.030openalex-snapshot
STWD-SFNN: Sequential three-way decisions with a single hidden layer feedforward neural network
inferred← citeshttps://doi.org/10.1016/j.ins.2025.122582openalex-snapshot
Mining frequent sequential patterns in fully dynamic database: A support approximation method
inferred← citeshttps://doi.org/10.1016/j.ins.2026.123940openalex-snapshot
Mining diversified top-k graph patterns by incorporating subjective interest
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.2026.104643openalex-snapshot
TaNSP: An efficient target pattern mining algorithm based on negative sequential pattern
inferred← citeshttps://doi.org/10.1016/j.knosys.2021.107361openalex-snapshot
HANP-Miner: High average utility nonoverlapping sequential pattern mining
inferred← citeshttps://doi.org/10.1016/j.knosys.2022.109710openalex-snapshot
Efficient mining of concept-hierarchy aware distinguishing sequential patterns
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.2025.114185openalex-snapshot
Skyline quantity-utility sequential pattern mining: An efficient and effective approach
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.knosys.2026.116419openalex-snapshot
Directly extracting maximal multi-level high-utility patterns
inferred← citeshttps://doi.org/10.1016/j.neucom.2021.12.075openalex-snapshot
DBC-Forest: Deep forest with binning confidence screening
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/icdm59182.2024.00028openalex-snapshot
Scalable Order-Preserving Pattern Mining
inferred← citeshttps://doi.org/10.1109/ickg52313.2021.00019openalex-snapshot
HSNP-Miner: High Utility Self-Adaptive Nonoverlapping Pattern Mining
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/ickg66886.2025.00035openalex-snapshot
A Survey of Repetitive Sequential Pattern Mining
inferred← citeshttps://doi.org/10.1109/tbdata.2026.3689016openalex-snapshot
Discovering High-Utility Sequential Rules With Increasing Utility Ratio
inferred← citeshttps://doi.org/10.1109/tcyb.2022.3169327openalex-snapshot
OPP-Miner: Order-Preserving Sequential 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/tetci.2026.3684476openalex-snapshot
OPM-Miner: One-Off Sequential Pattern Mining for Multivariate Time Series Analysis
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.2026.3705564openalex-snapshot
CoNR-Miner: Self-Adaptive Co-Occurrence Nonoverlapping Sequential Rule Mining
inferred← citeshttps://doi.org/10.1145/3532193openalex-snapshot
HW-Forest: Deep Forest with Hashing Screening and Window Screening
inferred← citeshttps://doi.org/10.1145/3532613openalex-snapshot
US-Rule: Discovering Utility-driven Sequential Rules
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ONP-Miner: One-off Negative Sequential Pattern Mining
inferred← citeshttps://doi.org/10.1145/3628450openalex-snapshot
Totally-ordered Sequential Rules for Utility Maximization
inferred← citeshttps://doi.org/10.1145/3658450openalex-snapshot
Co-occurrence Order-preserving Pattern Mining with Keypoint Alignment for Time Series
inferred← citeshttps://doi.org/10.1145/3716390openalex-snapshot
Mining Repetitive Negative Sequential Patterns with Gap Constraints
inferred← citeshttps://doi.org/10.1145/3744344openalex-snapshot
Towards Sequence Utility Maximization under Utility Occupancy Measure
inferred← citeshttps://doi.org/10.3233/ida-216325openalex-snapshot
NetNDP: Nonoverlapping (delta, gamma)-approximate pattern matching
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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Flexible Pattern Discovery and Analysis
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