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Scalable Mining of High-Utility Sequential Patterns With Three-Tier MapReduce Model
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/11430919_79openalex-snapshot
A Two-Phase Algorithm for Fast Discovery of High Utility Itemsets
inferredcites →https://doi.org/10.1007/978-3-319-18032-8_19openalex-snapshot
Mining Uncertain Sequential Patterns in Iterative MapReduce
inferredcites →https://doi.org/10.1007/978-3-319-18032-8_51openalex-snapshot
Mining High Utility Itemsets in Big Data
inferredcites →https://doi.org/10.1007/978-3-319-46131-1_8openalex-snapshot
The SPMF Open-Source Data Mining Library Version 2
inferredcites →https://doi.org/10.1007/978-3-319-64468-4_36openalex-snapshot
MapFIM: Memory Aware Parallelized Frequent Itemset Mining in Very Large Datasets
inferredcites →https://doi.org/10.1007/978-3-642-20149-3_18openalex-snapshot
Mining High Utility Mobile Sequential Patterns in Mobile Commerce Environments
inferredcites →https://doi.org/10.1007/978-3-642-31087-4_63openalex-snapshot
A One-Phase Method for Mining High Utility Mobile Sequential Patterns in Mobile Commerce Environments
inferredcites →https://doi.org/10.1007/978-3-662-58415-6_7openalex-snapshot
MapFIM+: Memory Aware Parallelized Frequent Itemset Mining In Very Large Datasets
inferredcites →https://doi.org/10.1007/s10115-015-0914-8openalex-snapshot
On efficiently mining high utility sequential patterns
inferredcites →https://doi.org/10.1007/s10115-016-0986-0openalex-snapshot
EFIM: a fast and memory efficient algorithm for high-utility itemset mining
inferredcites →https://doi.org/10.1016/j.bdr.2016.07.001openalex-snapshot
Approximate Parallel High Utility Itemset Mining
inferredcites →https://doi.org/10.1016/j.engappai.2018.10.010openalex-snapshot
Mining of skyline patterns by considering both frequent and utility constraints
inferredcites →https://doi.org/10.1016/j.eswa.2010.12.082openalex-snapshot
An effective tree structure for mining high utility itemsets
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.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.ins.2020.03.030openalex-snapshot
Efficient approach of recent high utility stream pattern mining with indexed list structure and pruning strategy considering arrival times of transactions
inferredcites →https://doi.org/10.1016/j.ins.2020.12.004openalex-snapshot
Fuzzy high-utility pattern mining in parallel and distributed Hadoop framework
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/10.1016/j.knosys.2020.106653openalex-snapshot
Damped sliding based utility oriented pattern mining over stream data
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/10.1109/69.250074openalex-snapshot
Database mining: a performance perspective
inferredcites →https://doi.org/10.1109/69.553155openalex-snapshot
Data mining: an overview from a database perspective
inferredcites →https://doi.org/10.1109/access.2020.2992729openalex-snapshot
A Multi-Core Approach to Efficiently Mining High-Utility Itemsets in Dynamic Profit Databases
inferredcites →https://doi.org/10.1109/bigdata.2013.6691742openalex-snapshot
Frequent Itemset Mining for Big Data
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/icdm.2012.20openalex-snapshot
Direct Discovery of High Utility Itemsets without Candidate Generation
inferredcites →https://doi.org/10.1109/icdm.2013.148openalex-snapshot
Efficiently Mining Top-K High Utility Sequential Patterns
inferredcites →https://doi.org/10.1109/icdmw.2007.115openalex-snapshot
HSN-PAM: Finding Hierarchical Probabilistic Groups from Large-Scale Networks
inferredcites →https://doi.org/10.1109/ifuzzy.2013.6825476openalex-snapshot
Mining high fuzzy utility sequential patterns
inferredcites →https://doi.org/10.1109/jsen.2020.2991045openalex-snapshot
A Pre-Large Weighted-Fusion System of Sensed High-Utility Patterns
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/tetci.2020.3000224openalex-snapshot
An Evolutionary Model to Mine High Expected Utility Patterns From Uncertain 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.2015.2510012openalex-snapshot
Mining High Utility Patterns in One Phase without Generating Candidates
inferredcites →https://doi.org/10.1137/1.9781611972740.51openalex-snapshot
A Foundational Approach to Mining Itemset Utilities from Databases
inferredcites →https://doi.org/10.1145/2184751.2184842openalex-snapshot
Apriori-based frequent itemset mining algorithms on MapReduce
inferredcites →https://doi.org/10.1145/2396761.2396773openalex-snapshot
Mining high utility itemsets without candidate generation
inferredcites →https://doi.org/10.1145/3311350.3347167openalex-snapshot
Factors to Consider for Tailored Gamification
inferredcites →https://doi.org/10.1145/3314107openalex-snapshot
A Survey of Parallel Sequential Pattern Mining
inferredcites →https://doi.org/10.1145/3363571openalex-snapshot
High-Utility Itemset Mining with Effective Pruning Strategies
inferredcites →https://doi.org/10.1186/s12918-017-0444-yopenalex-snapshot
Quantitative reproducibility analysis for identifying reproducible targets from high-throughput experiments
inferredcites →https://doi.org/10.1186/s12918-017-0475-4openalex-snapshot
Mining significant high utility gene regulation sequential patterns
inferredcites →https://doi.org/10.3390/s20041078openalex-snapshot
Efficient Algorithm for Mining Non-Redundant High-Utility Association Rules
inferredcites →https://doi.org/10.4218/etrij.10.1510.0066openalex-snapshot
A Novel Approach for Mining High-Utility Sequential Patterns in Sequence Databases
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Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Virtual Reality Video CU Partition Fast System Based on Dynamic Selection Algorithm
inferred← citeshttps://doi.org/10.1007/s10115-024-02105-7openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s10115-024-02185-5openalex-snapshot
An efficient approach for incremental erasable utility pattern mining from non-binary data
inferred← citeshttps://doi.org/10.1007/s10489-022-04436-wopenalex-snapshot
HDSHUI-miner: a novel algorithm for discovering spatial high-utility itemsets in high-dimensional spatiotemporal databases
inferred← citeshttps://doi.org/10.1007/s10489-023-04514-7openalex-snapshot
Mining inter-sequence patterns with Itemset constraints
inferred← citeshttps://doi.org/10.1007/s10489-023-04990-xopenalex-snapshot
F-RFM-Miner: an efficient algorithm for mining fuzzy patterns using the recency-frequency-monetary model
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inferred← citeshttps://doi.org/10.1016/j.artmed.2022.102347openalex-snapshot
Cardiovascular disease detection from high utility rare rule mining
inferred← citeshttps://doi.org/10.1016/j.eswa.2022.117253openalex-snapshot
Conversion paths of online consumers: A sequential pattern mining approach
inferred← citeshttps://doi.org/10.1016/j.ins.2022.07.132openalex-snapshot
Deep learning based hashtag recommendation system for multimedia data
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.2026.104637openalex-snapshot
DS_HURNSP: An effective method for mining high utility repeated negative sequential patterns from data streams
inferred← citeshttps://doi.org/10.1016/j.rser.2022.112782openalex-snapshot
Utilizing power consumption and SLA violations using dynamic VM consolidation in cloud data centers
inferred← citeshttps://doi.org/10.1051/e3sconf/202561903012openalex-snapshot
Q-Learning based VM Consolidation Approach for Enhancing Cloud Data Centres Power Efficiency
inferred← citeshttps://doi.org/10.1093/comjnl/bxac031openalex-snapshot
Mining Frequent Serial Positioning Episode Rules with Forward and Backward Search Technique from Event Sequences
inferred← citeshttps://doi.org/10.1109/access.2022.3179592openalex-snapshot
SGMiner: A Fast and Scalable GPU-Based Frequent Pattern Miner on SSDs
inferred← citeshttps://doi.org/10.1109/bdpc69980.2026.11608163openalex-snapshot
Mining Customer Lifecycle Advancement Patterns
inferred← citeshttps://doi.org/10.1109/dasc/picom/cbdcom/cy55231.2022.9927807openalex-snapshot
Mining of High-Utility Sequence Patterns in Large-Scale Uncertain Databases
inferred← citeshttps://doi.org/10.1109/icde65448.2025.00209openalex-snapshot
Indexing Strings with Utilities
inferred← citeshttps://doi.org/10.1109/icdmw58026.2022.00077openalex-snapshot
Large-Scale Sequential Utility Pattern Mining in Uncertain Environments
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.2023.3241213openalex-snapshot
MCoR-Miner: Maximal Co-Occurrence Nonoverlapping Sequential Rule Mining
inferred← citeshttps://doi.org/10.1109/tkde.2023.3256126openalex-snapshot
Mining High Utility Itemsets Using Prefix Trees and Utility Vectors
inferred← citeshttps://doi.org/10.1109/tkde.2023.3334300openalex-snapshot
RNP-Miner: Repetitive Nonoverlapping Sequential Pattern Mining
inferred← citeshttps://doi.org/10.1109/tsc.2025.3536359openalex-snapshot
Uncertainty-Driven Pattern Mining on Incremental Data for Stream Analyzing Service
inferred← citeshttps://doi.org/10.1111/exsy.70034openalex-snapshot
Recommendation of Learning Resources for MOOCs Based on Historical Sequential Behaviours
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Methods for Analyzing Medical-Order Sequence Variants in Sequential Pattern Mining for Electronic Medical Record Systems
inferred← citeshttps://doi.org/10.1145/3716390openalex-snapshot
Mining Repetitive Negative Sequential Patterns with Gap Constraints
inferred← citeshttps://doi.org/10.3233/jifs-232107openalex-snapshot
A survey of high utility sequential patterns mining methods
inferred← citeshttps://doi.org/10.3934/aci.2023004openalex-snapshot
From basic approaches to novel challenges and applications in Sequential Pattern Mining
inferred← citeshttps://doi.org/10.4018/978-1-7998-9220-5.ch010openalex-snapshot
Big Data Mining and Analytics With MapReduce
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