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OWSP-Miner: Self-adaptive One-off Weak-gap Strong Pattern Mining

https://doi.org/10.1145/3476247
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Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/sam.11192openalex-snapshot
Mining Compressing Sequential Patterns
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Mining Top-k Distinguishing Sequential Patterns with Flexible Gap Constraints
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Mining Top-k Distinguishing Temporal Sequential Patterns from Event Sequences
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CCPM: A Scalable and Noise-Resistant Closed Contiguous Sequential Patterns Mining Algorithm
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Efficient string matching with wildcards and length constraints
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Mining frequent and top-K High Utility Time Interval-based Events with Duration patterns
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High average-utility sequential pattern mining based on uncertain databases
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Pattern matching with wildcards and gap-length constraints based on a centrality-degree graph
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Mining high utility itemsets by dynamically pruning the tree structure
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Mining sequential patterns with periodic wildcard gaps
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Strict approximate pattern matching with general gaps
inferredcites →https://doi.org/10.1007/s10489-014-0630-1openalex-snapshot
Mining frequent closed inter-sequence patterns efficiently using dynamic bit vectors
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/s10489-020-01743-yopenalex-snapshot
Incrementally updating the high average-utility patterns with pre-large concept
inferredcites →https://doi.org/10.1007/s10489-020-01778-1openalex-snapshot
NetDAP: (δ, γ) −approximate pattern matching with length constraints
inferredcites →https://doi.org/10.1007/s10586-017-1671-0openalex-snapshot
Mining distinguishing subsequence patterns with nonoverlapping condition
inferredcites →https://doi.org/10.1007/s10618-014-0398-2openalex-snapshot
Mining outlying aspects on numeric data
inferredcites →https://doi.org/10.1007/s11432-015-0935-3openalex-snapshot
Strict pattern matching under non-overlapping condition
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Sentiment Classification Using Negative and Intensive Sentiment Supplement Information
inferredcites →https://doi.org/10.1016/j.compbiomed.2013.02.006openalex-snapshot
PMBC: Pattern mining from biological sequences with wildcard constraints
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Mining non-redundant closed flexible periodic patterns
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FMaxCloHUSM: An efficient algorithm for mining frequent closed and maximal high utility sequences
inferredcites →https://doi.org/10.1016/j.eswa.2009.06.064openalex-snapshot
Mining closed flexible patterns in time-series databases
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A novel approach for mining maximal frequent patterns
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SAX-ARM: Deviant event pattern discovery from multivariate time series using symbolic aggregate approximation and association rule 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.ins.2007.03.018openalex-snapshot
Efficient mining of weighted interesting patterns with a strong weight and/or support affinity
inferredcites →https://doi.org/10.1016/j.ins.2018.04.013openalex-snapshot
Frequent pattern discovery with tri-partition alphabets
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ProUM: Projection-based utility mining on sequence data
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Mining local periodic patterns in a discrete sequence
inferredcites →https://doi.org/10.1016/j.ipl.2017.03.001openalex-snapshot
On approximate pattern matching with thresholds
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Autonomic intrusion detection: Adaptively detecting anomalies over unlabeled audit data streams in computer networks
inferredcites →https://doi.org/10.1016/j.knosys.2016.09.013openalex-snapshot
Efficient mining of high-utility itemsets using multiple minimum utility thresholds
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.2017.12.029openalex-snapshot
Damped window based high average utility pattern mining over data streams
inferredcites →https://doi.org/10.1016/j.knosys.2019.03.022openalex-snapshot
Mining high-utility itemsets in dynamic profit databases
inferredcites →https://doi.org/10.1016/j.knosys.2019.07.018openalex-snapshot
Efficient high average-utility itemset mining using novel vertical weak upper-bounds
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/10.1109/access.2016.2593953openalex-snapshot
Discovering Patterns With Weak-Wildcard Gaps
inferredcites →https://doi.org/10.1109/dsc.2016.49openalex-snapshot
Frequent Pattern Mining Based on Approximate Edit Distance Matrix
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/tkde.2003.1209017openalex-snapshot
On-demand forecasting of stock prices using a real-time predictor
inferredcites →https://doi.org/10.1109/tkde.2013.109openalex-snapshot
Data mining with big data
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/tr.2018.2873427openalex-snapshot
Toward Better Summarizing Bug Reports With Crowdsourcing Elicited Attributes
inferredcites →https://doi.org/10.1145/2133360.2133362openalex-snapshot
Efficient Mining of Gap-Constrained Subsequences and Its Various Applications
inferredcites →https://doi.org/10.1145/2396761.2396773openalex-snapshot
Mining high utility itemsets without candidate generation
inferredcites →https://doi.org/10.1145/2783258.2783306openalex-snapshot
Discovery of Meaningful Rules in Time Series
inferredcites →https://doi.org/10.1145/3041218openalex-snapshot
Mining Resource Profiles from Event Logs
inferredcites →https://doi.org/10.1145/3106164openalex-snapshot
Fuzzy Clustering of Crowdsourced Test Reports for Apps
inferredcites →https://doi.org/10.1145/3386250openalex-snapshot
Predictive Cyber Situational Awareness and Personalized Blacklisting
inferredcites →https://doi.org/10.1145/3399671openalex-snapshot
Efficient Mining of Outlying Sequence Patterns for Analyzing Outlierness of Sequence Data
inferredcites →https://doi.org/10.1145/3439771openalex-snapshot
Exploring Decomposition for Solving Pattern Mining Problems
inferredcites →https://doi.org/10.1177/0165551515603286openalex-snapshot
Approximate pattern matching with gap constraints
inferredcites →https://doi.org/10.3233/ida-163231openalex-snapshot
Mining multi-relational high utility itemsets from star schemas
inferredcites →https://doi.org/10.3233/ida-173435openalex-snapshot
Efficient pattern matching with periodical wildcards in uncertain sequences
inferredcites →https://doi.org/10.5555/3225638.3225800openalex-snapshot
Mining minimal distinguishing subsequence patterns with gap constraints
inferredcites →https://doi.org/10.6688/jise.2014.30.3.7openalex-snapshot
Stock Trend Prediction by Using K-Means and AprioriAll Algorithm for Sequential Chart Pattern Mining

What points at this work

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

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/s10489-023-05118-xopenalex-snapshot
CCSMP: an efficient closed contiguous sequential pattern mining algorithm with a pattern relation graph
inferred← citeshttps://doi.org/10.1016/j.eswa.2026.134352openalex-snapshot
MOPP-Miner: Structure-constrained discovery of multivariate order-preserving offset patterns
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.2025.122871openalex-snapshot
OSP-Miner: Mining one-off weak-gap strong sequential patterns
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.2021.107361openalex-snapshot
HANP-Miner: High average utility nonoverlapping 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.2024.112398openalex-snapshot
Mining actionable repetitive positive and negative sequential patterns
inferred← citeshttps://doi.org/10.1016/j.knosys.2025.114803openalex-snapshot
CoTP-Miner: Co-occurrence three-way sequential pattern mining
inferred← citeshttps://doi.org/10.1109/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/tcyb.2022.3169327openalex-snapshot
OPP-Miner: Order-Preserving Sequential Pattern Mining for Time Series
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/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.2023.3334300openalex-snapshot
RNP-Miner: Repetitive Nonoverlapping Sequential Pattern Mining
inferred← citeshttps://doi.org/10.1109/tkde.2023.3335049openalex-snapshot
GAN-Based Temporal Association Rule Mining on Multivariate Time Series Data
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/10.1145/3460511openalex-snapshot
Pedestrian-Aware Panoramic Video Stitching Based on a Structured Camera Array
inferred← citeshttps://doi.org/10.1145/3480245openalex-snapshot
NTP-Miner: Nonoverlapping Three-Way Sequential Pattern Mining
inferred← citeshttps://doi.org/10.1145/3512468openalex-snapshot
Introduction to the Special Issue on Pattern-Driven Mining, Analytics, and Prediction for Decision Making, Part II
inferred← citeshttps://doi.org/10.1145/3549940openalex-snapshot
ONP-Miner: One-off Negative Sequential Pattern Mining
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/10.1145/3773899openalex-snapshot
Mining High Average Utility Nonoverlapping Patterns from Sequential Database
inferred← citeshttps://doi.org/10.3233/ida-216325openalex-snapshot
NetNDP: Nonoverlapping (delta, gamma)-approximate pattern matching
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