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A survey of incremental high‐utility itemset mining
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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.1216openalex-snapshot
Data mining in distributed environment: a survey
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/10.1007/978-3-319-18032-8_51openalex-snapshot
Mining High Utility Itemsets in Big Data
inferredcites →https://doi.org/10.1007/978-3-319-25159-2_20openalex-snapshot
iCHUM: An Efficient Algorithm for High Utility Mining in Incremental Databases
inferredcites →https://doi.org/10.1007/978-3-319-27060-9_44openalex-snapshot
EFIM: A Highly Efficient Algorithm for High-Utility Itemset Mining
inferredcites →https://doi.org/10.1007/s10115-012-0492-yopenalex-snapshot
An efficient projection-based indexing approach for mining high 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/s10115-016-0991-3openalex-snapshot
FDHUP: Fast algorithm for mining discriminative high utility patterns
inferredcites →https://doi.org/10.1007/s10489-013-0467-zopenalex-snapshot
Incrementally mining high utility patterns based on pre-large concept
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/10.1016/j.aei.2015.05.003openalex-snapshot
A fast updated algorithm to maintain the discovered high-utility itemsets for transaction modification
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.aei.2016.02.003openalex-snapshot
Fast algorithms for mining high-utility itemsets with various discount strategies
inferredcites →https://doi.org/10.1016/j.datak.2005.10.004openalex-snapshot
Mining itemset utilities from transaction databases
inferredcites →https://doi.org/10.1016/j.eswa.2007.04.009openalex-snapshot
Incrementally fast updated frequent pattern trees☆
inferredcites →https://doi.org/10.1016/j.eswa.2008.03.014openalex-snapshot
The Pre-FUFP algorithm for incremental mining
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.2011.01.006openalex-snapshot
Effective utility mining with the measure of average utility
inferredcites →https://doi.org/10.1016/j.eswa.2012.01.072openalex-snapshot
An incremental mining algorithm for high utility itemsets
inferredcites →https://doi.org/10.1016/j.eswa.2013.10.049openalex-snapshot
On-shelf utility mining with negative item values
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.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.ins.2017.02.058openalex-snapshot
A lattice-based approach for mining high utility association rules
inferredcites →https://doi.org/10.1016/j.knosys.2014.12.010openalex-snapshot
Top-k high utility pattern mining with effective threshold raising strategies
inferredcites →https://doi.org/10.1016/j.knosys.2016.08.022openalex-snapshot
FHN: An efficient algorithm for mining high-utility itemsets with negative unit profits
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.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.neucom.2013.01.056openalex-snapshot
Selecting the best measures to discover quantitative association rules
inferredcites →https://doi.org/10.1016/s1088-467x(99)00028-1openalex-snapshot
Mining association rules from quantitative data
inferredcites →https://doi.org/10.1023/a:1020991105855openalex-snapshot
Mining Fuzzy Multiple-Level Association Rules from Quantitative 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/cit.2007.120openalex-snapshot
CTU-Mine: An Efficient High Utility Itemset Mining Algorithm Using the Pattern Growth Approach
inferredcites →https://doi.org/10.1109/icde.1996.492094openalex-snapshot
Maintenance of discovered association rules in large databases: an incremental updating technique
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/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.2015.2458860openalex-snapshot
Efficient Algorithms for Mining Top-K High Utility Itemsets
inferredcites →https://doi.org/10.1137/1.9781611972740.51openalex-snapshot
A Foundational Approach to Mining Itemset Utilities from Databases
inferredcites →https://doi.org/10.1145/1352793.1352839openalex-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/10.1145/276304.276307openalex-snapshot
Exploratory mining and pruning optimizations of constrained associations rules
inferredcites →https://doi.org/10.1145/276305.276307openalex-snapshot
Exploratory mining and pruning optimizations of constrained associations rules
inferredcites →https://doi.org/10.1145/2818869.2818887openalex-snapshot
Efficient Incremental High Utility Itemset Mining
inferredcites →https://doi.org/10.1145/568574.568580openalex-snapshot
Constrained frequent pattern mining
inferredcites →https://doi.org/10.1155/2015/161564openalex-snapshot
An Incremental High‐Utility Mining Algorithm with Transaction Insertion
inferredcites →https://doi.org/10.1155/2015/306590openalex-snapshot
The Diophantine Equation 8x + py = z2
inferredcites →https://doi.org/10.3233/ida-160837openalex-snapshot
A fast maintenance algorithm of the discovered high-utility itemsets with transaction deletion
inferredcites →https://doi.org/10.3233/ida-2001-5203openalex-snapshot
A new incremental data mining algorithm using pre-large itemsets1
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1002/ett.4209openalex-snapshot
An evolutionary computation‐based privacy‐preserving data mining model under a multithreshold constraint
inferred← citeshttps://doi.org/10.1002/int.22799openalex-snapshot
Occupancy‐based utility pattern mining in dynamic environments of intelligent systems
inferred← citeshttps://doi.org/10.1002/spe.3165openalex-snapshot
Horn rule discovery with batched caching and rule identifier for proficient compressor of knowledge data
inferred← citeshttps://doi.org/10.1002/widm.1329openalex-snapshot
Frequent itemset mining: A 25 years review
inferred← citeshttps://doi.org/10.1007/978-3-030-04585-2_17openalex-snapshot
Mining High Utility Itemsets from Multiple Databases
inferred← citeshttps://doi.org/10.1007/978-3-030-16145-3_15openalex-snapshot
Efficiently Finding High Utility-Frequent Itemsets Using Cutoff and Suffix Utility
inferred← citeshttps://doi.org/10.1007/978-3-030-27618-8_26openalex-snapshot
Discovering Partial Periodic High Utility Itemsets in Temporal Databases
inferred← citeshttps://doi.org/10.1007/978-3-030-39197-3_11openalex-snapshot
Whitebox Induction of Default Rules Using High-Utility Itemset Mining
inferred← citeshttps://doi.org/10.1007/978-3-030-79457-6_1openalex-snapshot
Fast Mining of Top-k Frequent Balanced Association Rules
inferred← citeshttps://doi.org/10.1007/978-3-030-79457-6_5openalex-snapshot
Discovering Spatial High Utility Itemsets in High-Dimensional Spatiotemporal Databases
inferred← citeshttps://doi.org/10.1007/978-3-031-84353-2_25openalex-snapshot
Effective High Utility Itemsets Mining Algorithm for Incremental Databases
inferred← citeshttps://doi.org/10.1007/978-3-032-03406-9_26openalex-snapshot
Deep Learning Based Model Optimization Method for Mining High Utility Patterns in Large Datasets
inferred← citeshttps://doi.org/10.1007/978-3-032-24075-0_9openalex-snapshot
A Hybrid GA–PSO–CSA with Bit-Vector Optimization for High-Utility Itemset Mining
inferred← citeshttps://doi.org/10.1007/978-981-15-9739-8_48openalex-snapshot
An Adaptive Utility Quantification Strategy for Penetration Semantic Knowledge Mining
inferred← citeshttps://doi.org/10.1007/978-981-16-8048-9_6openalex-snapshot
Mining Frequency-Utility Patterns from a Big Data Environment
inferred← citeshttps://doi.org/10.1007/s10489-018-1227-xopenalex-snapshot
An efficient algorithm for mining periodic high-utility sequential patterns
inferred← citeshttps://doi.org/10.1007/s10489-019-01412-9openalex-snapshot
Applying uncertain frequent pattern mining to improve ranking of retrieved images
inferred← citeshttps://doi.org/10.1007/s10489-019-01524-2openalex-snapshot
Effective sanitization approaches to protect sensitive knowledge in high-utility itemset mining
inferred← citeshttps://doi.org/10.1007/s10489-020-01899-7openalex-snapshot
Mining frequent weighted closed itemsets using the WN-list structure and an early pruning strategy
inferred← citeshttps://doi.org/10.1007/s10489-020-01994-9openalex-snapshot
Fast Top-K association rule mining using rule generation property pruning
inferred← citeshttps://doi.org/10.1007/s10489-021-02574-1openalex-snapshot
FRI-miner: fuzzy rare itemset mining
inferred← citeshttps://doi.org/10.1007/s10489-021-02751-2openalex-snapshot
Analytics of high average-utility patterns in the industrial internet of things
inferred← citeshttps://doi.org/10.1007/s10489-021-02922-1openalex-snapshot
Discovery of closed high utility itemsets using a fast nature-inspired ant colony algorithm
inferred← citeshttps://doi.org/10.1007/s10489-022-03978-3openalex-snapshot
An incremental rare association rule mining approach with a life cycle tree structure considering time-sensitive 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/s10586-021-03448-4openalex-snapshot
Incremental mining of high utility sequential patterns using MapReduce paradigm
inferred← citeshttps://doi.org/10.1007/s10844-021-00647-8openalex-snapshot
Mining emotion-aware sequential rules at user-level from micro-blogs
inferred← citeshttps://doi.org/10.1007/s11227-021-04218-0openalex-snapshot
OHUQI: Mining on-shelf high-utility quantitative itemsets
inferred← citeshttps://doi.org/10.1007/s12652-020-01706-8openalex-snapshot
RETRACTED ARTICLE: An improved memory adaptive up-growth to mine high utility itemsets from large transaction databases
inferred← citeshttps://doi.org/10.1007/s40815-018-0520-5openalex-snapshot
Efficiently Updating the Discovered Multiple Fuzzy Frequent Itemsets with Transaction Insertion
inferred← citeshttps://doi.org/10.1007/s41060-024-00659-xopenalex-snapshot
Sequential pattern mining algorithms and their applications: a technical review
inferred← citeshttps://doi.org/10.1016/j.asoc.2022.109073openalex-snapshot
High-utility itemsets mining based on binary particle swarm optimization with multiple adjustment strategies
inferred← citeshttps://doi.org/10.1016/j.engappai.2018.10.010openalex-snapshot
Mining of skyline patterns by considering both frequent and utility constraints
inferred← citeshttps://doi.org/10.1016/j.engappai.2021.104516openalex-snapshot
On-shelf utility mining from transaction database
inferred← citeshttps://doi.org/10.1016/j.engappai.2022.105480openalex-snapshot
Performing in-situ analytics: Mining frequent patterns from big IoT data at network edge with D-HARPP
inferred← citeshttps://doi.org/10.1016/j.engappai.2023.106182openalex-snapshot
Mining periodic high-utility itemsets with both positive and negative utilities
inferred← citeshttps://doi.org/10.1016/j.engappai.2023.107047openalex-snapshot
Targeted mining of top- k high utility itemsets
inferred← citeshttps://doi.org/10.1016/j.engappai.2024.109960openalex-snapshot
Fast mining local high-utility itemsets
inferred← citeshttps://doi.org/10.1016/j.eswa.2020.113441openalex-snapshot
Swarm optimized cluster based framework for information retrieval
inferred← citeshttps://doi.org/10.1016/j.future.2018.12.029openalex-snapshot
Efficient approach for incremental high utility pattern mining with indexed list structure
inferred← citeshttps://doi.org/10.1016/j.future.2019.04.027openalex-snapshot
Seal imprint verification via feature analysis and classifications
inferred← citeshttps://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
inferred← citeshttps://doi.org/10.1016/j.future.2020.11.026openalex-snapshot
Hiding sensitive information in eHealth datasets
inferred← citeshttps://doi.org/10.1016/j.future.2021.08.028openalex-snapshot
Reliable customer analysis using federated learning and exploring deep-attention edge intelligence
inferred← citeshttps://doi.org/10.1016/j.heliyon.2024.e37761openalex-snapshot
IPHM: Incremental periodic high-utility mining algorithm in dynamic and evolving data environments
inferred← citeshttps://doi.org/10.1016/j.ins.2019.05.006openalex-snapshot
An efficient method for mining high utility closed itemsets
inferred← citeshttps://doi.org/10.1016/j.ins.2019.07.005openalex-snapshot
Correlated utility-based pattern mining
inferred← citeshttps://doi.org/10.1016/j.ins.2019.10.033openalex-snapshot
ProUM: Projection-based utility mining on sequence data
inferred← citeshttps://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
inferred← citeshttps://doi.org/10.1016/j.ins.2020.10.001openalex-snapshot
Discovering high utility-occupancy patterns from uncertain data
inferred← citeshttps://doi.org/10.1016/j.ins.2021.04.035openalex-snapshot
TKUS: Mining top-k high utility sequential patterns
inferred← citeshttps://doi.org/10.1016/j.ins.2021.10.010openalex-snapshot
Mining weighted sequential patterns in incremental uncertain databases
inferred← citeshttps://doi.org/10.1016/j.ins.2022.11.009openalex-snapshot
Fuzzy-driven periodic frequent pattern mining
inferred← citeshttps://doi.org/10.1016/j.ins.2022.12.092openalex-snapshot
Efficient mining of top-k high utility itemsets through genetic algorithms
inferred← citeshttps://doi.org/10.1016/j.ins.2023.02.034openalex-snapshot
Parallel incremental association rule mining framework for public opinion analysis
inferred← citeshttps://doi.org/10.1016/j.is.2021.101908openalex-snapshot
Pattern on demand in transactional distributed databases
inferred← citeshttps://doi.org/10.1016/j.knosys.2018.11.019openalex-snapshot
Towards efficiently mining closed high utility itemsets from incremental databases
inferred← citeshttps://doi.org/10.1016/j.knosys.2019.07.018openalex-snapshot
Efficient high average-utility itemset mining using novel vertical weak upper-bounds
inferred← citeshttps://doi.org/10.1016/j.knosys.2019.105138openalex-snapshot
Discriminative Streaming Network Embedding
inferred← citeshttps://doi.org/10.1016/j.knosys.2020.106064openalex-snapshot
Mining top-rank-k frequent weighted itemsets using WN-list structures and an early pruning strategy
inferred← citeshttps://doi.org/10.1016/j.knosys.2020.106653openalex-snapshot
Damped sliding based utility oriented pattern mining over stream data
inferred← citeshttps://doi.org/10.1016/j.knosys.2022.109194openalex-snapshot
Negative pattern discovery with individual support
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/10.1017/s0269888925000013openalex-snapshot
Mining of high utility itemsets from incremental datasets: a survey
inferred← citeshttps://doi.org/10.1017/s1471068420000356openalex-snapshot
White-box Induction From SVM Models: Explainable AI with Logic Programming
inferred← citeshttps://doi.org/10.1108/k-06-2020-0365openalex-snapshot
Analysis of patent documents with utility mining: a case study of wind energy technology
inferred← citeshttps://doi.org/10.1109/access.2019.2899831openalex-snapshot
A Sanitization Approach to Secure Shared Data in an IoT Environment
inferred← citeshttps://doi.org/10.1109/access.2019.2929082openalex-snapshot
LUIM: New Low-Utility Itemset Mining Framework
inferred← citeshttps://doi.org/10.1109/access.2019.2958150openalex-snapshot
Improved Genetic Algorithm for High-Utility Itemset Mining
inferred← citeshttps://doi.org/10.1109/access.2020.2970181openalex-snapshot
Efficient Discovery of Weighted Frequent Neighborhood Itemsets in Very Large Spatiotemporal Databases
inferred← citeshttps://doi.org/10.1109/access.2020.2979289openalex-snapshot
Efficient Approach for Damped Window-Based High Utility Pattern Mining With List Structure
inferred← citeshttps://doi.org/10.1109/bibm47256.2019.8983219openalex-snapshot
Proximity Search Method for Mining Biomedical and Genomic Information
inferred← citeshttps://doi.org/10.1109/bigdata.2018.8622242openalex-snapshot
CoUPM: Correlated Utility-based Pattern Mining
inferred← citeshttps://doi.org/10.1109/bigdata.2018.8622405openalex-snapshot
Privacy Preserving Utility Mining: A Survey
inferred← citeshttps://doi.org/10.1109/bigdata47090.2019.9006152openalex-snapshot
Utility-Driven Mining of High Utility Episodes
inferred← citeshttps://doi.org/10.1109/bigdata47090.2019.9006171openalex-snapshot
A GA-based Framework for Mining High Fuzzy Utility Itemsets
inferred← citeshttps://doi.org/10.1109/bigdata50022.2020.9377761openalex-snapshot
OSUMI: On-Shelf Utility Mining from Itemset-based Data
inferred← citeshttps://doi.org/10.1109/bigdata50022.2020.9377946openalex-snapshot
Distributed Mining of Spatial High Utility Itemsets in Very Large Spatiotemporal Databases using Spark In-Memory Computing Architecture
inferred← citeshttps://doi.org/10.1109/bigdata52589.2021.9671342openalex-snapshot
Joint Utility and Frequency for Pattern Classification
inferred← citeshttps://doi.org/10.1109/bigdata52589.2021.9671720openalex-snapshot
Targeted High-Utility Itemset Querying
inferred← citeshttps://doi.org/10.1109/bigdata52589.2021.9671912openalex-snapshot
Discovering Top-k Spatial High Utility Itemsets in Very Large Quantitative Spatiotemporal databases
inferred← citeshttps://doi.org/10.1109/bigdata52589.2021.9672021openalex-snapshot
Mining On-shelf High-utility Quantitative Itemsets
inferred← citeshttps://doi.org/10.1109/bigdata52589.2021.9672064openalex-snapshot
Discovering Relative High Utility Itemsets in Very Large Transactional Databases Using Null-Invariant Measure
inferred← citeshttps://doi.org/10.1109/bigdata55660.2022.10020226openalex-snapshot
Targeted Mining of Rare High-Utility Patterns
inferred← citeshttps://doi.org/10.1109/bigdata55660.2022.10020765openalex-snapshot
Pattern Discovery with Utility Occupancy
inferred← citeshttps://doi.org/10.1109/bigdata55660.2022.10021132openalex-snapshot
Flexibly Mining Better Patterns
inferred← citeshttps://doi.org/10.1109/bigdata59044.2023.10386702openalex-snapshot
Mining Rare Utility Patterns within Target Items
inferred← citeshttps://doi.org/10.1109/bigdata59044.2023.10386927openalex-snapshot
Targeted Querying of Closed High-Utility Itemsets
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/ds-rt47707.2019.8958680openalex-snapshot
Synthesization of High-Utility Patterns in Parallel Computing
inferred← citeshttps://doi.org/10.1109/dsaa60987.2023.10302500openalex-snapshot
Incremental Targeted Mining in Sequences
inferred← citeshttps://doi.org/10.1109/fuzz48607.2020.9177543openalex-snapshot
Mining Multiple Fuzzy Frequent Patterns with Compressed List Structures
inferred← citeshttps://doi.org/10.1109/icdis55630.2022.00060openalex-snapshot
Behavioral Sequence Classification with Constraints
inferred← citeshttps://doi.org/10.1109/icdis55630.2022.00062openalex-snapshot
Fuzzy Utility Mining on Temporal Data
inferred← citeshttps://doi.org/10.1109/icdmw.2019.00067openalex-snapshot
Comparative Study on Trajectory Outlier Detection Algorithms
inferred← citeshttps://doi.org/10.1109/icdmw.2019.00143openalex-snapshot
Discovering Spatial Weighted Frequent Itemsets in Spatiotemporal Databases
inferred← citeshttps://doi.org/10.1109/icdmw53433.2021.00081openalex-snapshot
TopUMS: Top-k Utility Mining in Stream Data
inferred← citeshttps://doi.org/10.1109/icdmw58026.2022.00076openalex-snapshot
Skyline Pattern Mining by Quantity-Utility Constraints in Large-Scale Databases
inferred← citeshttps://doi.org/10.1109/ispa-bdcloud-socialcom-sustaincom59178.2023.00136openalex-snapshot
FSKY-Miner: Fast Mining of Skyline Patterns
inferred← citeshttps://doi.org/10.1109/jsen.2020.2991045openalex-snapshot
A Pre-Large Weighted-Fusion System of Sensed High-Utility Patterns
inferred← citeshttps://doi.org/10.1109/jsyst.2020.2979279openalex-snapshot
Utility-Driven Data Analytics on Uncertain Data
inferred← citeshttps://doi.org/10.1109/smc.2019.8914402openalex-snapshot
ProUM: High Utility Sequential Pattern Mining
inferred← citeshttps://doi.org/10.1109/smc42975.2020.9282878openalex-snapshot
UHUOPM: High Utility Occupancy Pattern Mining in Uncertain Data
inferred← citeshttps://doi.org/10.1109/taai48200.2019.8959890openalex-snapshot
A Project-based PMiner Algorithm in Uncertain Databases
inferred← citeshttps://doi.org/10.1109/tai.2022.3171530openalex-snapshot
Targeted High-Utility Itemset Querying
inferred← citeshttps://doi.org/10.1109/tai.2022.3223965openalex-snapshot
Discovering High Utility Episodes in Sequences
inferred← citeshttps://doi.org/10.1109/tcyb.2019.2896267openalex-snapshot
HUOPM: High-Utility Occupancy Pattern Mining
inferred← citeshttps://doi.org/10.1109/tcyb.2020.2970176openalex-snapshot
Fast Utility Mining on Sequence Data
inferred← citeshttps://doi.org/10.1109/tetci.2020.3032701openalex-snapshot
Security and Privacy in Shared HitLCPS Using a GA-Based Multiple-Threshold Sanitization Model
inferred← citeshttps://doi.org/10.1109/tkde.2019.2942594openalex-snapshot
A Survey of Utility-Oriented Pattern Mining
inferred← citeshttps://doi.org/10.1109/tkde.2020.3033519openalex-snapshot
Mining High Quality Patterns Using Multi-Objective Evolutionary Algorithm
inferred← citeshttps://doi.org/10.1109/tkde.2021.3139086openalex-snapshot
Anomaly Rule Detection in Sequence Data
inferred← citeshttps://doi.org/10.1109/tnse.2023.3278674openalex-snapshot
Data Analytic for Healthcare Cyber Physical System
inferred← citeshttps://doi.org/10.1109/tsc.2023.3294945openalex-snapshot
An Approach for Incremental Mining of Clickstream Patterns as a Service Application
inferred← citeshttps://doi.org/10.1142/s0218126622502619openalex-snapshot
Efficient Uncertain Sequence Pattern Mining Based on Hadoop Platform
inferred← citeshttps://doi.org/10.1145/3314107openalex-snapshot
A Survey of Parallel Sequential Pattern Mining
inferred← citeshttps://doi.org/10.1145/3335783.3335789openalex-snapshot
Discovering Spatial High Utility Itemsets in Spatiotemporal Databases
inferred← citeshttps://doi.org/10.1145/3362070openalex-snapshot
Utility Mining across Multi-Sequences with Individualized Thresholds
inferred← citeshttps://doi.org/10.1145/3363571openalex-snapshot
High-Utility Itemset Mining with Effective Pruning Strategies
inferred← citeshttps://doi.org/10.1145/3391251openalex-snapshot
Utility-Driven Mining of Trend Information for Intelligent System
inferred← citeshttps://doi.org/10.1145/3408296openalex-snapshot
A Multi-Threshold Ant Colony System-based Sanitization Model in Shared Medical Environments
inferred← citeshttps://doi.org/10.1145/3427477.3429990openalex-snapshot
A Deep Q-Learning Sanitization Approach for Privacy Preserving Data Mining
inferred← citeshttps://doi.org/10.1145/3446938openalex-snapshot
Utility Mining Across Multi-Dimensional Sequences
inferred← citeshttps://doi.org/10.1145/3457570openalex-snapshot
On-Shelf Utility Mining of Sequence Data
inferred← citeshttps://doi.org/10.1145/3487553.3524706openalex-snapshot
Discovering Top-k Profitable Patterns for Smart Manufacturing
inferred← citeshttps://doi.org/10.1145/3487553.3524707openalex-snapshot
Fast RFM Model for Customer Segmentation
inferred← citeshttps://doi.org/10.1145/3487553.3524708openalex-snapshot
Mining with Rarity for Web Intelligence
inferred← citeshttps://doi.org/10.1145/3488058openalex-snapshot
Towards Revenue Maximization with Popular and Profitable Products
inferred← citeshttps://doi.org/10.1145/3511808.3557327openalex-snapshot
Frequent Itemset Mining with Local Differential Privacy
inferred← citeshttps://doi.org/10.1145/3531480openalex-snapshot
Smart System: Joint Utility and Frequency for Pattern Classification
inferred← citeshttps://doi.org/10.1145/3543873.3587586openalex-snapshot
Weighted Statistically Significant Pattern Mining
inferred← citeshttps://doi.org/10.1145/3597935openalex-snapshot
HUSP-SP: Faster Utility Mining on Sequence Data
inferred← citeshttps://doi.org/10.1155/2022/1942517openalex-snapshot
An Incremental Interesting Maximal Frequent Itemset Mining Based on FP‐Growth Algorithm
inferred← citeshttps://doi.org/10.1155/2022/6705986openalex-snapshot
Media Content Mining Based on Artificial Intelligence and Network Interaction
inferred← citeshttps://doi.org/10.2174/9798898812133125040010openalex-snapshot
Fake Profile Detection in Social Media: Incremental Greedy Ensemble Approach
inferred← citeshttps://doi.org/10.2298/csis220615040wopenalex-snapshot
The effective skyline quantify-utility patterns mining algorithm with pruning strategies
inferred← citeshttps://doi.org/10.31202/ecjse.1075528openalex-snapshot
Yüksek Faydalı Öğe Kümeleri için Veri Madenciliği Algoritmalarının Analizi Üzerine Bir Anket
inferred← citeshttps://doi.org/10.3233/jifs-179666openalex-snapshot
An efficient algorithm for fuzzy frequent itemset mining
inferred← citeshttps://doi.org/10.3233/jifs-202745openalex-snapshot
A Survey of incremental high-utility pattern mining based on storage structure
inferred← citeshttps://doi.org/10.3390/app132212340openalex-snapshot
Mining Top-k High Average-Utility Sequential Patterns for Resource Transformation
inferred← citeshttps://doi.org/10.3390/app9040774openalex-snapshot
A Grid-Based Swarm Intelligence Algorithm for Privacy-Preserving Data Mining
inferred← citeshttps://doi.org/10.3390/sym14102032openalex-snapshot
An Efficient Algorithm for Mining Stable Periodic High-Utility Sequential Patterns
inferred← citeshttps://doi.org/10.3934/mbe.2019082openalex-snapshot
The density-based clustering method for privacy-preserving data mining
inferred← citeshttps://doi.org/10.4018/ijsi.303579openalex-snapshot
Sliding Window-Based High Utility Item-Sets Mining Over Data Stream Using Extended Global Utility Item-Sets Tree
inferred← citeshttps://doi.org/10.48550/arxiv.1904.12248openalex-snapshot
Fast Utility Mining on Complex Sequences
inferred← citeshttps://doi.org/10.48550/arxiv.1905.11226openalex-snapshot
Induction of Non-Monotonic Rules From Statistical Learning Models Using High-Utility Itemset Mining
inferred← citeshttps://doi.org/10.48550/arxiv.2106.14811openalex-snapshot
TOPIC: Top-k High-Utility Itemset Discovering
inferred← citeshttps://doi.org/10.48550/arxiv.2106.14830openalex-snapshot
THUE: Discovering Top-K High Utility Episodes
inferred← citeshttps://doi.org/10.48550/arxiv.2111.00307openalex-snapshot
FUIM: Fuzzy Utility Itemset Mining
inferred← citeshttps://doi.org/10.48550/arxiv.2111.15026openalex-snapshot
Anomaly Rule Detection in Sequence Data
inferred← citeshttps://doi.org/10.5815/ijigsp.2022.05.06openalex-snapshot
Sliding Window Based High Utility Item-Sets Mining over Data Stream Using Extended Global Utility Item-Sets Tree
inferred← citeshttps://doi.org/10.59628/jast.v1i4.663openalex-snapshot
Analysis Of Mining Algorithms for Patterns of Frequent Item sets and Hidden
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