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https://doi.org/10.1145/3430767
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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/10.1007/978-3-319-43946-4_1openalex-snapshot
Mining Recent High-Utility Patterns from Temporal Databases with Time-Sensitive Constraint
inferredcites →https://doi.org/10.1007/s00521-018-3876-4openalex-snapshot
Minimal weighted infrequent itemset mining-based outlier detection approach on uncertain data stream
inferredcites →https://doi.org/10.1007/s10115-016-0991-3openalex-snapshot
FDHUP: Fast algorithm for mining discriminative high utility patterns
inferredcites →https://doi.org/10.1007/s10489-014-0601-6openalex-snapshot
Incremental high utility pattern mining with static and dynamic databases
inferredcites →https://doi.org/10.1007/s10489-018-1280-5openalex-snapshot
SPPC: a new tree structure for mining erasable patterns in data streams
inferredcites →https://doi.org/10.1007/s10489-018-1316-xopenalex-snapshot
TKEH: an efficient algorithm for mining top-k high utility itemsets
inferredcites →https://doi.org/10.1007/s10489-019-01412-9openalex-snapshot
Applying uncertain frequent pattern mining to improve ranking of retrieved images
inferredcites →https://doi.org/10.1007/s40815-018-0520-5openalex-snapshot
Efficiently Updating the Discovered Multiple Fuzzy Frequent Itemsets with Transaction Insertion
inferredcites →https://doi.org/10.1016/j.aei.2015.06.002openalex-snapshot
Efficient algorithms for mining up-to-date high-utility patterns
inferredcites →https://doi.org/10.1016/j.camwa.2008.10.060openalex-snapshot
Mining frequent closed itemsets from a landmark window over online data streams
inferredcites →https://doi.org/10.1016/j.engappai.2015.06.009openalex-snapshot
RWFIM: Recent weighted-frequent itemsets mining
inferredcites →https://doi.org/10.1016/j.engappai.2017.09.010openalex-snapshot
Efficient algorithms for mining top-rank- k erasable patterns using pruning strategies and the subsume concept
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.2012.03.062openalex-snapshot
Interactive mining of high utility patterns over data streams
inferredcites →https://doi.org/10.1016/j.eswa.2016.03.001openalex-snapshot
High utility pattern mining over data streams with sliding window technique
inferredcites →https://doi.org/10.1016/j.eswa.2017.10.039openalex-snapshot
A weighted N-list-based method for mining frequent weighted itemsets
inferredcites →https://doi.org/10.1016/j.eswa.2018.09.051openalex-snapshot
Mining top-k high utility itemsets with effective threshold raising strategies
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.future.2018.12.029openalex-snapshot
Efficient approach for incremental high utility pattern mining with indexed list structure
inferredcites →https://doi.org/10.1016/j.ins.2013.09.007openalex-snapshot
Mining frequent items in data stream using time fading model
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.jss.2007.07.026openalex-snapshot
An efficient algorithm for mining temporal high utility itemsets from data streams
inferredcites →https://doi.org/10.1016/j.knosys.2016.10.027openalex-snapshot
An ACO-based approach to mine high-utility itemsets
inferredcites →https://doi.org/10.1016/j.knosys.2017.03.016openalex-snapshot
An efficient algorithm for mining high utility patterns from incremental databases with one database scan
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.2018.05.014openalex-snapshot
High utility drift detection in quantitative data streams
inferredcites →https://doi.org/10.1109/access.2018.2801261openalex-snapshot
MEMU: More Efficient Algorithm to Mine High Average-Utility Patterns With Multiple Minimum Average-Utility Thresholds
inferredcites →https://doi.org/10.1109/access.2019.2919524openalex-snapshot
Incremental Mining of High Utility Patterns in One Phase by Absence and Legacy-Based Pruning
inferredcites →https://doi.org/10.1109/access.2020.2976662openalex-snapshot
Efficient Chain Structure for High-Utility Sequential Pattern Mining
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/access.2020.2997409openalex-snapshot
Approximation of Probabilistic Maximal Frequent Itemset Mining Over Uncertain Sensed Data
inferredcites →https://doi.org/10.1109/tcyb.2019.2896267openalex-snapshot
HUOPM: High-Utility Occupancy Pattern Mining
inferredcites →https://doi.org/10.1109/tie.2017.2682782openalex-snapshot
Efficient High Utility Pattern Mining for Establishing Manufacturing Plans With Sliding Window Control
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.2018.2833478openalex-snapshot
Efficient Vertical Mining of High Average-Utility Itemsets Based on Novel Upper-Bounds
inferredcites →https://doi.org/10.1145/2396761.2396773openalex-snapshot
Mining high utility itemsets without candidate generation
inferredcites →https://doi.org/10.1145/2700484openalex-snapshot
Significant Correlation Pattern Mining in Smart Homes
inferredcites →https://doi.org/10.1145/3173458openalex-snapshot
Mining Significant Microblogs for Misinformation Identification
inferredcites →https://doi.org/10.1145/3178114openalex-snapshot
On Incremental High Utility Sequential Pattern Mining
inferredcites →https://doi.org/10.1145/3200764openalex-snapshot
Interactive Visual Graph Mining and Learning
inferredcites →https://doi.org/10.1145/3326163openalex-snapshot
Large-Scale Frequent Episode Mining from Complex Event Sequences with Hierarchies
inferredcites →https://doi.org/10.1504/ijcse.2019.099640openalex-snapshot
A model of mining approximate frequent itemsets using rough set theory
inferredcites →https://doi.org/10.1504/ijhpcn.2018.093234openalex-snapshot
Mining frequent itemsets over uncertain data streams
inferredcites →https://doi.org/10.3233/ida-160861openalex-snapshot
Mining high utility itemsets based on the time decaying model

What points at this work

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

inferred← citeshttps://doi.org/10.1007/s10115-022-01763-9openalex-snapshot
An efficient algorithm for mining closed high utility itemsets over data streams with one dataset scan
inferred← citeshttps://doi.org/10.1007/s10115-023-01831-8openalex-snapshot
FCHM-stream: fast closed high utility itemsets mining over data streams
inferred← citeshttps://doi.org/10.1007/s10115-023-02045-8openalex-snapshot
Mining Top-K constrained cross-level high-utility itemsets over data streams
inferred← citeshttps://doi.org/10.1007/s10115-026-02900-4openalex-snapshot
High utility itemsets mining in data stream using cuckoo search
inferred← citeshttps://doi.org/10.1007/s10489-022-03164-5openalex-snapshot
An efficient and scalable approach for mining subgraphs in a single large graph
inferred← citeshttps://doi.org/10.1007/s10489-022-03250-8openalex-snapshot
A framework for itemset placement with diversification for retail businesses
inferred← citeshttps://doi.org/10.1007/s10489-023-04925-6openalex-snapshot
High utility pattern mining algorithm over data streams using ext-list.
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
inferred← citeshttps://doi.org/10.1007/s10489-025-06729-2openalex-snapshot
Efficient high utility itemsets mining over data streams with compact utility list structure
inferred← citeshttps://doi.org/10.1007/s41060-024-00619-5openalex-snapshot
Incremental mining algorithms for generating and updating frequent patterns for dynamic databases against insert, update, and support changes
inferred← citeshttps://doi.org/10.1016/j.asoc.2025.112902openalex-snapshot
Efficient fuzzy-based high utility pattern computing and analyzing approach with temporal properties
inferred← citeshttps://doi.org/10.1016/j.datak.2026.102647openalex-snapshot
A matrix-based high-utility itemsets mining algorithm over streaming data using sliding windows
inferred← citeshttps://doi.org/10.1016/j.engappai.2026.114511openalex-snapshot
Damped window based high occupancy pattern mining with one scanning of data streams
inferred← citeshttps://doi.org/10.1016/j.eswa.2021.115741openalex-snapshot
Efficient algorithms for mining closed high utility itemsets in dynamic profit databases
inferred← citeshttps://doi.org/10.1016/j.eswa.2022.118214openalex-snapshot
EHMIN: Efficient approach of list based high-utility pattern mining with negative unit profits
inferred← citeshttps://doi.org/10.1016/j.eswa.2023.120935openalex-snapshot
A multi-population particle swarm optimization-based time series predictive technique
inferred← citeshttps://doi.org/10.1016/j.eswa.2024.124383openalex-snapshot
MLC-miner: Efficiently discovering multi-level closed high utility patterns from quantitative hierarchical transaction databases
inferred← citeshttps://doi.org/10.1016/j.eswa.2025.129309openalex-snapshot
Uncertainty oriented pattern extracting and analyzing via sliding window control
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.2023.01.003openalex-snapshot
The stream data warehouse: Page replacement algorithms and quality of service metrics
inferred← citeshttps://doi.org/10.1016/j.ins.2025.122243openalex-snapshot
Sliding window-based high utility occupancy pattern mining for data streams
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.knosys.2023.110478openalex-snapshot
Pre-large based high utility pattern mining for transaction insertions in incremental database
inferred← citeshttps://doi.org/10.1016/j.knosys.2023.111060openalex-snapshot
Efficient approach for mining high-utility patterns on incremental databases with dynamic profits
inferred← citeshttps://doi.org/10.1016/j.knosys.2025.113359openalex-snapshot
An efficient method for mining top-k multi-level high utility itemsets
inferred← citeshttps://doi.org/10.1016/j.knosys.2025.114453openalex-snapshot
Utility and occupancy driven pattern analysis for processing dynamic data streams in damped window control
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.2026.116812openalex-snapshot
A unified framework for occurrence-support Repetitive Sequential Pattern Mining in static and sliding-window streams
inferred← citeshttps://doi.org/10.1016/j.patcog.2025.112283openalex-snapshot
Fusion of window controls to recognize and analyze erasable patterns over data streams
inferred← citeshttps://doi.org/10.1038/s41598-024-60279-0openalex-snapshot
Incremental high average-utility itemset mining: survey and challenges
inferred← citeshttps://doi.org/10.1109/access.2021.3131577openalex-snapshot
Efficient Methods for Clickstream Pattern Mining on Incremental Databases
inferred← citeshttps://doi.org/10.1109/access.2023.3271864openalex-snapshot
Efficient Method for Mining High Utility Occupancy Patterns Based on Indexed List Structure
inferred← citeshttps://doi.org/10.1109/access.2024.3406562openalex-snapshot
Incremental Top-k High Utility Pattern Mining and Analyzing Over the Entire Accumulated Dynamic Database
inferred← citeshttps://doi.org/10.1109/access.2025.3543755openalex-snapshot
Incremental Closed Frequent Itemsets Mining-Based Approach Using Maximal Candidates
inferred← citeshttps://doi.org/10.1109/icirca51532.2021.9544949openalex-snapshot
Construction of Japanese Interactive Analysis System Based on Perception Model and Pattern Mining
inferred← citeshttps://doi.org/10.1109/icpeca60615.2024.10470947openalex-snapshot
An Improved FP-Growth Algorithm with Time Decay Factor and Element Attention Weight
inferred← citeshttps://doi.org/10.1109/imcom69009.2026.11360905openalex-snapshot
Mining Damped High Utility Occupancy Patterns Over Data Streams
inferred← citeshttps://doi.org/10.1109/jiot.2025.3555278openalex-snapshot
Pre-Eminent Utility Driven Data Analytics Based on Prelarge Patterns for Dynamic Transaction Deletion in IoT Environments
inferred← citeshttps://doi.org/10.1109/tcyb.2022.3198661openalex-snapshot
Scalable and Efficient Approach for High Temporal Fuzzy Utility Pattern Mining
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.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.2024.3438274openalex-snapshot
OPF-Miner: Order-Preserving Pattern Mining With Forgetting Mechanism for Time Series
inferred← citeshttps://doi.org/10.1145/3549940openalex-snapshot
ONP-Miner: One-off Negative Sequential Pattern Mining
inferred← citeshttps://doi.org/10.1145/3773899openalex-snapshot
Mining High Average Utility Nonoverlapping Patterns from Sequential Database
inferred← citeshttps://doi.org/10.1186/s40537-025-01352-xopenalex-snapshot
Intelligent average utility pattern analysis using pre-large concept in dynamic stream data
inferred← citeshttps://doi.org/10.1371/journal.pone.0317427openalex-snapshot
Multi-level high utility-itemset hiding
inferred← citeshttps://doi.org/10.18488/79.v9i1.3236openalex-snapshot
High Utility Pattern Mining: A Survey on Current and Possible Areas of Applications
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