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Average utility driven data analytics on damped windows for intelligent systems with data streams

https://doi.org/10.1002/int.22528
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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/int.21644openalex-snapshot
Upgrading a Granular Computing Based Data Mining Framework to a Relational Case
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OWA Operator-Based Hybrid Framework for Outlier Reduction in Web Mining
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Properties of a Granular Computing Framework for Mining Relational Data
inferredcites →https://doi.org/10.1002/int.21899openalex-snapshot
Empirical Data Analytics
inferredcites →https://doi.org/10.1002/int.21917openalex-snapshot
Fuzzy Quantification and Opinion Mining on Qualitative Data using Feature Reduction
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Real‐time force doors detection system using distributed sensors and neural networks
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Measuring global prosperity using data envelopment analysis and OWA operator
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A decision‐making algorithm for online shopping using deep‐learning–based opinion pairs mining and q ‐rung orthopair fuzzy interaction Heronian mean operators
inferredcites →https://doi.org/10.1002/int.22269openalex-snapshot
Efficiently mining erasable stream patterns for intelligent systems over uncertain data
inferredcites →https://doi.org/10.1002/int.22294openalex-snapshot
SAT‐based and CP‐based declarative approaches for Top‐Rank‐ K closed frequent itemset mining
inferredcites →https://doi.org/10.1007/11430919_79openalex-snapshot
A Two-Phase Algorithm for Fast Discovery of High Utility Itemsets
inferredcites →https://doi.org/10.1007/978-3-319-08326-1_9openalex-snapshot
FHM: Faster High-Utility Itemset Mining Using Estimated Utility Co-occurrence Pruning
inferredcites →https://doi.org/10.1007/978-3-319-42007-3_11openalex-snapshot
FHM $$+$$ : Faster High-Utility Itemset Mining Using Length Upper-Bound Reduction
inferredcites →https://doi.org/10.1007/978-3-662-45237-0_5openalex-snapshot
A New Method for Mining High Average Utility Itemsets
inferredcites →https://doi.org/10.1007/s10115-016-0989-xopenalex-snapshot
Indexed list-based high utility pattern mining with utility upper-bound reduction and pattern combination techniques
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/s10489-016-0890-zopenalex-snapshot
Efficient algorithm for mining high average-utility itemsets in incremental transaction databases
inferredcites →https://doi.org/10.1007/s10489-017-0896-1openalex-snapshot
A fast algorithm for mining high average-utility itemsets
inferredcites →https://doi.org/10.1007/s10489-017-1047-4openalex-snapshot
ETARM: an efficient top-k association rule mining algorithm
inferredcites →https://doi.org/10.1007/s10489-018-1155-9openalex-snapshot
Top-k high average-utility itemsets mining with effective pruning strategies
inferredcites →https://doi.org/10.1016/j.aei.2016.04.002openalex-snapshot
An efficient algorithm to mine high average-utility itemsets
inferredcites →https://doi.org/10.1016/j.eswa.2014.11.001openalex-snapshot
Pruning strategies for mining high utility itemsets
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.2018.10.047openalex-snapshot
Data mining based on clustering and association rule analysis for knowledge discovery in multiobjective topology optimization
inferredcites →https://doi.org/10.1016/j.eswa.2019.01.035openalex-snapshot
Association rule mining based parameter adaptive strategy for differential evolution algorithms
inferredcites →https://doi.org/10.1016/j.future.2016.09.007openalex-snapshot
A new efficient approach for mining uncertain frequent patterns using minimum data structure without false positives
inferredcites →https://doi.org/10.1016/j.future.2016.10.027openalex-snapshot
Mining of high average-utility itemsets using novel list structure and pruning strategy
inferredcites →https://doi.org/10.1016/j.future.2017.07.035openalex-snapshot
Single-pass based efficient erasable pattern mining using list data structure on dynamic incremental databases
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.2018.09.066openalex-snapshot
Top-k frequent items and item frequency tracking over sliding windows of any size
inferredcites →https://doi.org/10.1016/j.ins.2019.01.056openalex-snapshot
EHNL: An efficient algorithm for mining high utility itemsets with negative utility value and length constraints
inferredcites →https://doi.org/10.1016/j.is.2005.04.001openalex-snapshot
Finding recently frequent itemsets adaptively over online transactional data streams,
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.2019.03.022openalex-snapshot
Mining high-utility itemsets in dynamic profit databases
inferredcites →https://doi.org/10.1109/access.2018.2820740openalex-snapshot
TUB-HAUPM: Tighter Upper Bound for Mining High Average-Utility Patterns
inferredcites →https://doi.org/10.1109/access.2019.2930004openalex-snapshot
Frequent Pattern Mining on Time and Location Aware Air Quality Data
inferredcites →https://doi.org/10.1109/i-span.2009.24openalex-snapshot
An Incremental Mining Algorithm for High Average-Utility Itemsets
inferredcites →https://doi.org/10.1109/icsmc.2009.5346333openalex-snapshot
Mining high average-utility itemsets
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/10.1142/s0219622012500307openalex-snapshot
EFFICIENTLY MINING HIGH AVERAGE-UTILITY ITEMSETS WITH AN IMPROVED UPPER-BOUND STRATEGY
inferredcites →https://doi.org/10.1145/1352793.1352839openalex-snapshot
Efficient algorithms for incremental utility mining
inferredcites →https://doi.org/10.1145/2396761.2396773openalex-snapshot
Mining high utility itemsets without candidate generation
inferredcites →https://doi.org/10.1145/335191.335372openalex-snapshot
Mining frequent patterns without candidate generation
inferredcites →https://doi.org/10.3233/ida-160861openalex-snapshot
Mining high utility itemsets based on the time decaying model
inferredcites →https://doi.org/10.3233/ifs-162106openalex-snapshot
Mining recent high average utility patterns based on sliding window from stream data

What points at this work

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

inferred← citeshttps://doi.org/10.1002/int.22841openalex-snapshot
Deep reinforcement learning‐based multitask hybrid computing offloading for multiaccess edge computing
inferred← citeshttps://doi.org/10.1007/978-981-92-5644-0_23openalex-snapshot
An Efficient Algorithm for Mining High Average-Efficiency Patterns over Data Streams
inferred← citeshttps://doi.org/10.1007/s10115-024-02288-zopenalex-snapshot
High utility itemset mining in data stream using elephant herding optimization
inferred← citeshttps://doi.org/10.1007/s10489-022-03580-7openalex-snapshot
A new approach for efficiently mining frequent weighted utility patterns
inferred← citeshttps://doi.org/10.1007/s10489-023-04554-zopenalex-snapshot
Mining frequent weighted utility patterns with dynamic weighted items from quantitative databases
inferred← citeshttps://doi.org/10.1007/s10489-026-07130-3openalex-snapshot
Efficient mining of compact high average utility patterns using the tightest weak lower bound
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.aej.2026.03.002openalex-snapshot
Exploring and detecting utility streams on prelarge and time fading properties
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.engappai.2025.113639openalex-snapshot
Mining high average-efficiency itemsets based on a compact list structure
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.2023.120697openalex-snapshot
An advanced approach for incremental flexible periodic pattern mining on time-series data
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.2025.107970openalex-snapshot
DFPM: Damped window-based flexible periodic pattern analysis on the time-decaying model
inferred← citeshttps://doi.org/10.1016/j.ins.2023.119924openalex-snapshot
Efficient approach of high average utility pattern mining with indexed list-based structure in dynamic environments
inferred← citeshttps://doi.org/10.1016/j.knosys.2022.109702openalex-snapshot
Efficient approach of sliding window-based high average-utility pattern mining with list structures
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.2025.113273openalex-snapshot
Efficient mining top-k high utility itemsets in incremental databases based on threshold raising strategies and pre-large concept
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.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.2023.3271864openalex-snapshot
Efficient Method for Mining High Utility Occupancy Patterns Based on Indexed List Structure
inferred← citeshttps://doi.org/10.1109/dsaa60987.2023.10302550openalex-snapshot
Enhanced Mining of High Utility Patterns from Streams of Dynamic Profit
inferred← citeshttps://doi.org/10.1109/rivf55975.2022.10013859openalex-snapshot
Mining Frequent Weighted Utility Patterns in Dynamic Quantitative Databases
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/10.1371/journal.pone.0317427openalex-snapshot
Multi-level high utility-itemset hiding
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Outlier Detection in Streaming Data for Telecommunications and Industrial Applications: A Survey
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