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Frequent itemset mining: A 25 years review
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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/10.1007/3-540-46027-6openalex-snapshot
Association Rule Mining: Models and Algorithms
inferredcites →https://doi.org/10.1007/978-3-030-04921-8_1openalex-snapshot
A Survey of High Utility Itemset Mining
inferredcites →https://doi.org/10.1007/978-3-319-33858-3openalex-snapshot
Pattern Mining with Evolutionary Algorithms
inferredcites →https://doi.org/10.1007/978-3-319-54430-4_7openalex-snapshot
MapReduce-Based Frequent Pattern Mining Framework with Multiple Item Support
inferredcites →https://doi.org/10.1007/978-3-319-98140-6openalex-snapshot
Supervised Descriptive Pattern Mining
inferredcites →https://doi.org/10.1007/978-3-540-68125-0_75openalex-snapshot
Mining a Complete Set of Both Positive and Negative Association Rules from Large Databases
inferredcites →https://doi.org/10.1007/978-3-540-73053-8_60openalex-snapshot
A Possibilistic Approach for Mining Uncertain Temporal Relations from Diagnostic Evolution Databases
inferredcites →https://doi.org/10.1007/978-3-642-05179-1_16openalex-snapshot
Simple Algorithms for Frequent Item Set Mining
inferredcites →https://doi.org/10.1007/978-3-7091-6230-9_65openalex-snapshot
Mining Numeric Association Rules with Genetic Algorithms
inferredcites →https://doi.org/10.1007/s00500-005-0476-xopenalex-snapshot
An efficient genetic algorithm for automated mining of both positive and negative quantitative association rules
inferredcites →https://doi.org/10.1007/s10115-011-0419-zopenalex-snapshot
Design and behavior study of a grammar-guided genetic programming algorithm for mining association rules
inferredcites →https://doi.org/10.1007/s10115-016-1004-2openalex-snapshot
The (black) art of runtime evaluation: Are we comparing algorithms or implementations?
inferredcites →https://doi.org/10.1007/s10462-018-9629-zopenalex-snapshot
Algorithms for frequent itemset mining: a literature review
inferredcites →https://doi.org/10.1007/s10586-018-1812-0openalex-snapshot
BIGMiner: a fast and scalable distributed frequent pattern miner for big data
inferredcites →https://doi.org/10.1007/s11227-013-0937-4openalex-snapshot
High performance evaluation of evolutionary-mined association rules on GPUs
inferredcites →https://doi.org/10.1007/s13748-016-0090-4openalex-snapshot
Pattern mining: current status and emerging topics
inferredcites →https://doi.org/10.1016/j.bdr.2017.06.006openalex-snapshot
Frequent Itemsets Mining for Big Data: A Comparative Analysis
inferredcites →https://doi.org/10.1016/j.eswa.2008.01.028openalex-snapshot
Genetic algorithm-based strategy for identifying association rules without specifying actual minimum support
inferredcites →https://doi.org/10.1016/j.ins.2018.07.020openalex-snapshot
Exploiting GPU and cluster parallelism in single scan frequent itemset mining
inferredcites →https://doi.org/10.1016/j.knosys.2018.04.037openalex-snapshot
MRQAR: A generic MapReduce framework to discover quantitative association rules in big data problems
inferredcites →https://doi.org/10.1016/j.patcog.2007.02.003openalex-snapshot
High-utility pattern mining: A method for discovery of high-utility item sets
inferredcites →https://doi.org/10.1016/s0167-739x(97)00019-8openalex-snapshot
Mining generalized association rules
inferredcites →https://doi.org/10.1016/s0306-4379(03)00072-3openalex-snapshot
Selecting the right objective measure for association analysis
inferredcites →https://doi.org/10.1017/s0269888912000331openalex-snapshot
A survey of frequent subgraph mining algorithms
inferredcites →https://doi.org/10.1023/a:1009773317876openalex-snapshot
Parallel Algorithms for Discovery of Association Rules
inferredcites →https://doi.org/10.1080/18756891.2016.1256573openalex-snapshot
Recommending degree studies according to students’ attitudes in high school by means of subgroup discovery
inferredcites →https://doi.org/10.1093/bioinformatics/18.10.1406openalex-snapshot
Identifying good diagnostic gene groups from gene expression profiles using the concept of emerging patterns
inferredcites →https://doi.org/10.1109/69.846291openalex-snapshot
Scalable algorithms for association mining
inferredcites →https://doi.org/10.1109/bigdata.2013.6691742openalex-snapshot
Frequent Itemset Mining for Big Data
inferredcites →https://doi.org/10.1109/icde.1998.655812openalex-snapshot
Mining for strong negative associations in a large database of customer transactions
inferredcites →https://doi.org/10.1109/icdm.2001.989494openalex-snapshot
On effective conceptual indexing and similarity search in text data
inferredcites →https://doi.org/10.1109/icdm.2001.989550openalex-snapshot
H-mine: hyper-structure mining of frequent patterns in large databases
inferredcites →https://doi.org/10.1109/icdm.2001.989600openalex-snapshot
Fast parallel association rule mining without candidacy generation
inferredcites →https://doi.org/10.1109/icsmc.2010.5641778openalex-snapshot
Parallel frequent patterns mining algorithm on GPU
inferredcites →https://doi.org/10.1109/ideas.1998.694360openalex-snapshot
Mining association rules with weighted items
inferredcites →https://doi.org/10.1109/mic.2013.10openalex-snapshot
Finding the Needle in the Big Data Systems Haystack
inferredcites →https://doi.org/10.1109/pdis.1996.568665openalex-snapshot
A fast distributed algorithm for mining association rules
inferredcites →https://doi.org/10.1109/sbac-pad.2010.15openalex-snapshot
Tree Projection-Based Frequent Itemset Mining on Multicore CPUs and GPUs
inferredcites →https://doi.org/10.1109/tcyb.2015.2496175openalex-snapshot
Speeding-Up Association Rule Mining With Inverted Index Compression
inferredcites →https://doi.org/10.1109/tcyb.2017.2751081openalex-snapshot
Apriori Versions Based on MapReduce for Mining Frequent Patterns on Big Data
inferredcites →https://doi.org/10.1111/j.1468-0394.2012.00627.xopenalex-snapshot
Association rule mining using genetic programming to provide feedback to instructors from multiple‐choice quiz data
inferredcites →https://doi.org/10.1145/170035.170072openalex-snapshot
Mining association rules between sets of items in large databases
inferredcites →https://doi.org/10.1145/1754428.1754432openalex-snapshot
Data mining for discrimination discovery
inferredcites →https://doi.org/10.1145/2184751.2184842openalex-snapshot
Apriori-based frequent itemset mining algorithms on MapReduce
inferredcites →https://doi.org/10.1145/223784.223813openalex-snapshot
An effective hash-based algorithm for mining association rules
inferredcites →https://doi.org/10.1145/253260.253325openalex-snapshot
Dynamic itemset counting and implication rules for market basket data
inferredcites →https://doi.org/10.1145/253262.253325openalex-snapshot
Dynamic itemset counting and implication rules for market basket data
inferredcites →https://doi.org/10.1145/277651.277694openalex-snapshot
Asynchronous parallel algorithm for mining association rules on a shared-memory multi-processors
inferredcites →https://doi.org/10.1145/335191.335372openalex-snapshot
Mining frequent patterns without candidate generation
inferredcites →https://doi.org/10.1145/568271.223813openalex-snapshot
An effective hash-based algorithm for mining association rules
inferredcites →https://doi.org/10.1145/956750.956788openalex-snapshot
Fast vertical mining using diffsets
inferredcites →https://doi.org/10.1201/ebk1439826119openalex-snapshot
Knowledge Discovery from Data Streams
inferredcites →https://doi.org/10.12733/jics20103619openalex-snapshot
An Efficient Algorithm of Frequent Itemsets Mining Based on MapReduce
inferredcites →https://doi.org/10.1613/jair.1089openalex-snapshot
Expert-Guided Subgroup Discovery: Methodology and Application
inferredcites →https://doi.org/10.2991/ijcis.2018.25905182openalex-snapshot
Optimization of quality measures in association rule mining: an empirical study
inferredcites →https://doi.org/10.3233/ica-140467openalex-snapshot
Reducing gaps in quantitative association rules: A genetic programming free-parameter algorithm
inferredcites →https://doi.org/10.3233/ica-170555openalex-snapshot
Mining association rules on Big Data through MapReduce genetic programming
inferredcites →https://doi.org/10.5860/choice.49-3305openalex-snapshot
Data mining: concepts and techniques
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Mining frequent approximate patterns in large networks
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Improving the understanding of cancer in a descriptive way: An emerging pattern mining‐based approach
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On the Use of Answer Set Programming for Model-Based Diagnosis
inferred← citeshttps://doi.org/10.1007/978-3-030-55789-8_71openalex-snapshot
TKE: Mining Top-K Frequent Episodes
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Mining Cross-Level High Utility Itemsets
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A Survey of Machine Learning for Network Fault Management
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Discovering Alarm Correlation Rules for Network Fault Management
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Towards Efficient Discovery of Periodic-Frequent Patterns in Columnar Temporal Databases
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Discovering Spatial High Utility Itemsets in High-Dimensional Spatiotemporal Databases
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Mining Skyline Frequent-Utility Itemsets with Utility Filtering
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Discovering Periodic-Frequent Patterns in Uncertain Temporal Databases
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Parallel High Utility Itemset Mining
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Towards Efficient Discovery of Periodic-Frequent Patterns in Dense Temporal Databases Using Complements
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A Robust Privacy Preserving Approach for Sanitizing Transaction Databases from Sensitive High Utility Patterns
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Towards Efficient Discovery of Partial Periodic Patterns in Columnar Temporal Databases
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Mining $$\epsilon $$-Closed High Utility Co-location Patterns from Spatial Data
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Discovering Top-k Periodic-Frequent Patterns in Very Large Temporal Databases
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Discovering Geo-referenced Frequent Patterns in Uncertain Geo-referenced Transactional Databases
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Efficiently Discovering Spatial Prevalent Co-location Patterns Without Distance Thresholds
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ChronoEdgeMiner: A Novel Algorithm for Extracting Frequent Temporal Graphs from Data
inferred← citeshttps://doi.org/10.1007/978-3-031-92545-0_45openalex-snapshot
Method for Mining Multi-level High-Efficiency Itemsets from the Taxonomy Database
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A Knowledge Representation Approach for Reasoning with Adaptation Rules
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Conciseness Effect on High Utility Itemsets Mining
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An Efficient Federated Utility-Mining Algorithm
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Efficient Extraction of Top-K High-Utility Itemsets Under Periodicity Constraints from Transactional Time Series with Negative-Utility Items
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Efficient Discovery of High-Occupancy Patterns in Large Datasets via MapReduce
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Introduction to Data Mining
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Discovering Frequent Patterns in Very Large Transactional Databases
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Finding Periodic Patterns in Multiple Sequences
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Discovering Periodic High Utility Itemsets in a Discrete Sequence
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Significant Association Rule Mining Without Support and Confidence Thresholds
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Adaptive GPU Compute Resource Allocation for Efficient High-Utility Itemset Mining
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Fuzzy Partial Periodic Frequent Pattern Mining in Quantitative Temporal Databases
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Beyond Air Pollution Prediction: A Step to Pollution Pattern Discovery with a Novel Binary Neuro-Symbolic AI Framework
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Periodic Frequent Pattern Mining with Consideration of Periodic Improvements for Business Promotion
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A Survey on Occupancy-Based Pattern Mining
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Ontology-Based Profiling by Hierarchical Cluster Analysis for Forecasting on Patterns of Significant Events
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UPFP-growth++: An Efficient Algorithm to Find Periodic-Frequent Patterns in Uncertain Temporal Databases
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An Improved Apriori Algorithm for Interestingness of Association Rules: A Case Study on the Mushroom Dataset
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High-utility itemsets mining integrating an improved crow search algorithm and particle search optimization
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Concise and interpretable multi-label rule sets
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Efficient mining of top-K cross-level high utility itemsets on unstable profit databases
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MoMAC: Multi-objective optimization to combine multiple association rules into an interpretable classification
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A Recursive Algorithm for Mining Association Rules
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Summarization and visualization of multi-level and multi-dimensional itemsets
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Efficient top-k high utility itemset mining on massive data
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What to expect from a set of itemsets?
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Fuzzy-driven periodic frequent pattern mining
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Efficient mining of top-k high utility itemsets through genetic algorithms
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An end-to-end knowledge graph solution to the frequent itemset hiding problem
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Data heterogeneity's impact on the performance of frequent itemset mining algorithms
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SeqRFM: Fast RFM analysis in sequence data
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Efficient algorithms for mining top-k high occupancy itemsets
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Support estimation in frequent itemsets mining on Enriched Two Level Tree
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Mining high utility itemsets using extended chain structure and utility machine
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Mining frequent weighted utility itemsets in hierarchical quantitative databases
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An efficient strategy for mining high-efficiency itemsets in quantitative databases
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Effective approaches for mining correlated and low-average-cost patterns
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An efficient algorithm for fast discovery of high-efficiency patterns
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An efficient method for mining top-k multi-level high utility itemsets
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Beyond trivial edges: A fractional approach to cohesive subgraph detection in hypergraphs
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An efficient method for maximal erasable itemset mining in incremental databases
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A New Branch and Bound Algorithm for Mining Frequent Conceptual Links in Social Networks
inferred← citeshttps://doi.org/10.1016/j.ress.2025.111804openalex-snapshot
DK-TGAN-ERRA: A data-knowledge-driven tabular GAN for synthesising cases applied to emergency rescue risk analysis
inferred← citeshttps://doi.org/10.1016/j.softx.2025.102341openalex-snapshot
PyAerial: Scalable association rule mining from tabular data
inferred← citeshttps://doi.org/10.1038/s41598-024-69071-6openalex-snapshot
Exploring the predictive factors of heart disease using rare association rule mining
inferred← citeshttps://doi.org/10.1038/s41598-025-00602-5openalex-snapshot
Research on association analysis between electricity consumption behaviors and weather factors based on mapreduce
inferred← citeshttps://doi.org/10.1061/jmenea.meeng-5514openalex-snapshot
Detecting Red-Flag Bidding Patterns in Low-Bid Procurement for Highway Projects with Pattern Mining
inferred← citeshttps://doi.org/10.1063/5.0248078openalex-snapshot
An exploration of clustering techniques for customer behaviour
inferred← citeshttps://doi.org/10.1080/01969722.2023.2296244openalex-snapshot
A Nature-Inspired Method to Mine Top-k Multi-Level High-Utility Itemsets
inferred← citeshttps://doi.org/10.1080/03772063.2020.1838343openalex-snapshot
Mining Frequent Patterns Partially Devoid of Dissociation with Automated MMS Specification Strategy
inferred← citeshttps://doi.org/10.1093/nargab/lqab114openalex-snapshot
OLOGRAM-MODL: mining enrichedn-wise combinations of genomic features with Monte Carlo and dictionary learning
inferred← citeshttps://doi.org/10.1093/pnasnexus/pgae517openalex-snapshot
Food choice mimicry on a large university campus
inferred← citeshttps://doi.org/10.1101/2022.11.19.517174openalex-snapshot
Mesoscopic patterns of functional connectivity alterations in autism by contrast subgraphs
inferred← citeshttps://doi.org/10.1109/access.2020.2980952openalex-snapshot
Association Rule Mining Method Based on the Similarity Metric of Tuple-Relation in Indoor Environment
inferred← citeshttps://doi.org/10.1109/access.2020.3010852openalex-snapshot
Extracting User-Centric Knowledge on Two Different Spaces: Concepts and Records
inferred← citeshttps://doi.org/10.1109/access.2020.3029302openalex-snapshot
Efficient Mining of Frequent Itemsets Using Only One Dynamic Prefix Tree
inferred← citeshttps://doi.org/10.1109/access.2020.3034885openalex-snapshot
Exceptional in so Many Ways—Discovering Descriptors That Display Exceptional Behavior on Contrasting Scenarios
inferred← citeshttps://doi.org/10.1109/access.2021.3056191openalex-snapshot
Identification of Changes in VLE Stakeholders’ Behavior Over Time Using Frequent Patterns Mining
inferred← citeshttps://doi.org/10.1109/access.2022.3202307openalex-snapshot
Pruning Strategy on Adaptive Rule Model by Sorting Utility Items
inferred← citeshttps://doi.org/10.1109/access.2023.3241313openalex-snapshot
Finding Stable Periodic-Frequent Itemsets in Big Columnar Databases
inferred← citeshttps://doi.org/10.1109/access.2023.3325839openalex-snapshot
k -PFPMiner: Top-k Periodic Frequent Patterns in Big Temporal Databases
inferred← citeshttps://doi.org/10.1109/access.2023.3326419openalex-snapshot
Mining Periodic-Frequent Patterns in Irregular Dense Temporal Databases Using Set Complements
inferred← citeshttps://doi.org/10.1109/access.2024.3357139openalex-snapshot
Efficient Frequent Chronicle Mining Algorithms: Application to Sleep Disorder
inferred← citeshttps://doi.org/10.1109/access.2024.3376477openalex-snapshot
Probabilistic Support Prediction: Fast Frequent Itemset Mining in Dense Data
inferred← citeshttps://doi.org/10.1109/access.2025.3581769openalex-snapshot
A Novel Depth-First Search Algorithm for Partial Periodic-Frequent Pattern Mining in Temporal Databases
inferred← citeshttps://doi.org/10.1109/aixb65684.2025.00027openalex-snapshot
Leveraging LLM's and Utility Metrics for Analyzing Prescription Drug Expenditure Patterns
inferred← citeshttps://doi.org/10.1109/bdcat56447.2022.00017openalex-snapshot
Extracting Relations Between Sectors
inferred← citeshttps://doi.org/10.1109/bdpc69980.2026.11608163openalex-snapshot
Mining Customer Lifecycle Advancement Patterns
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/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/bigdata50022.2020.9378215openalex-snapshot
Discovering Closed Periodic-Frequent Patterns in Very Large Temporal Databases
inferred← citeshttps://doi.org/10.1109/bigdata52589.2021.9671342openalex-snapshot
Joint Utility and Frequency for Pattern Classification
inferred← citeshttps://doi.org/10.1109/bigdata52589.2021.9671556openalex-snapshot
Discovering Maximal Partial Periodic Patterns in Very Large Temporal Databases
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/bigdata52589.2021.9672064openalex-snapshot
Discovering Relative High Utility Itemsets in Very Large Transactional Databases Using Null-Invariant Measure
inferred← citeshttps://doi.org/10.1109/bigdata55660.2022.10020216openalex-snapshot
A Novel GPU-Accelerated Algorithm to Discover Periodic-Frequent Patterns in Temporal Databases
inferred← citeshttps://doi.org/10.1109/bigdata66926.2025.11401810openalex-snapshot
Rescan-Amended Federated Utility Mining
inferred← citeshttps://doi.org/10.1109/bigdata66926.2025.11402218openalex-snapshot
Accelerating Fuzzy Frequent Pattern Mining on GPUs with GPU Direct Storage
inferred← citeshttps://doi.org/10.1109/cisct57197.2023.10351486openalex-snapshot
BigHeuFIM: A Heuristic Apriori Algorithm for Extraction of Frequent Itemsets on Big Data
inferred← citeshttps://doi.org/10.1109/cmss69636.2026.11688858openalex-snapshot
Pattern Mining in Superconducting Materials Using Frequent, Closed, and Maximal Itemset Analysis
inferred← citeshttps://doi.org/10.1109/dsaa49011.2020.00013openalex-snapshot
Discovering Maximal Periodic-Frequent Patterns in Very Large Temporal Databases
inferred← citeshttps://doi.org/10.1109/fuzz-ieee55066.2022.9882785openalex-snapshot
Discovering Fuzzy Geo-referenced Periodic-Frequent Patterns in Geo-referenced Time Series Databases
inferred← citeshttps://doi.org/10.1109/fuzz45933.2021.9494594openalex-snapshot
Discovering Fuzzy Frequent Spatial Patterns in Large Quantitative Spatiotemporal databases
inferred← citeshttps://doi.org/10.1109/icaibd57115.2023.10206354openalex-snapshot
Elite Frequent-Utility Pattern Mining in Hadoop Environments
inferred← citeshttps://doi.org/10.1109/icccnt51525.2021.9580115openalex-snapshot
Significant Itemset Mining with Support-Attractiveness Framework
inferred← citeshttps://doi.org/10.1109/icde55515.2023.00384openalex-snapshot
Efficient Parallel Mining of High-utility Itemsets on Multicore Processors
inferred← citeshttps://doi.org/10.1109/icde55515.2023.00388openalex-snapshot
Efficient Parallel Mining of High-utility Itemsets on Multicore Processors
inferred← citeshttps://doi.org/10.1109/icde60146.2024.00069openalex-snapshot
Online Detection of Outstanding Quantiles with QuantileFilter
inferred← citeshttps://doi.org/10.1109/icdh72779.2026.00016openalex-snapshot
Pattern Discovery in Digital Health Data for the Early Detection of Juvenile Arthritis
inferred← citeshttps://doi.org/10.1109/icdm54844.2022.00147openalex-snapshot
Estimating the Pruned Search Space Size of Subgroup Discovery
inferred← citeshttps://doi.org/10.1109/icdmw51313.2020.00095openalex-snapshot
TKC: Mining Top-K Cross-Level High Utility Itemsets
inferred← citeshttps://doi.org/10.1109/icdmw53433.2021.00080openalex-snapshot
A Unified Framework to Discover Partial Periodic-Frequent Patterns in Row and Columnar Temporal Databases
inferred← citeshttps://doi.org/10.1109/icdmw58026.2022.00075openalex-snapshot
Mining Valuable Fuzzy Patterns via the RFM Model
inferred← citeshttps://doi.org/10.1109/iciccs51141.2021.9432237openalex-snapshot
Significant Association Rule Mining with High Associability
inferred← citeshttps://doi.org/10.1109/icicec62498.2024.10808562openalex-snapshot
High Utility Item Set Mining in Transactional Data using a Deep Learning Model with Particle Swarm Optimization
inferred← citeshttps://doi.org/10.1109/icicis66182.2025.11313139openalex-snapshot
The Evolution of Pattern Mining: From Frequent to High-Utility to High-Efficiency Patterns
inferred← citeshttps://doi.org/10.1109/icpads60453.2023.00081openalex-snapshot
Fast RFM Analysis in Sequence Data
inferred← citeshttps://doi.org/10.1109/icwr65219.2025.11006167openalex-snapshot
A Comprehensive Review on Frequent Pattern Mining Algorithms in Data Mining
inferred← citeshttps://doi.org/10.1109/ipdps65963.2026.00035openalex-snapshot
Looking for (Genomic) Needles in a Haystack: Sparsity-Driven Search for Identifying Correlated Genetic Mutations in Cancer
inferred← citeshttps://doi.org/10.1109/iscaie51753.2021.9431821openalex-snapshot
Association Rule Mining using FP-Growth Algorithm to Prevent Maverick Buying
inferred← citeshttps://doi.org/10.1109/iwcia52852.2021.9626042openalex-snapshot
Shared-Memory Parallelization of FP-growth with Dynamic Load Estimation and Balancing
inferred← citeshttps://doi.org/10.1109/iwcmc61514.2024.10592472openalex-snapshot
Cross-Layer Alarm Association Rules Discovery of Cloud-Network based on Knowledge Graph
inferred← citeshttps://doi.org/10.1109/mcsoc67473.2025.00024openalex-snapshot
PAMI-GPT: A Conversational Custom-GPT Model for Pattern Mining
inferred← citeshttps://doi.org/10.1109/sose50414.2020.9130523openalex-snapshot
Correlation Analysis for Complex Equipment Systems Based on Multi-objective Association Rule Mining
inferred← citeshttps://doi.org/10.1109/tase.2023.3293843openalex-snapshot
Operating Optimization of Steam Turbine Unit Based on Big Data Parallel Association Rule Mining
inferred← citeshttps://doi.org/10.1109/tbdata.2026.3689016openalex-snapshot
Discovering High-Utility Sequential Rules With Increasing Utility Ratio
inferred← citeshttps://doi.org/10.1109/tcyb.2021.3109066openalex-snapshot
An Integrated Cluster Detection, Optimization, and Interpretation Approach for Financial Data
inferred← citeshttps://doi.org/10.1109/tfuzz.2025.3574149openalex-snapshot
Efficient Discovery of Fuzzy Partial Periodic Frequent Patterns Within Quantitative Temporal Databases
inferred← citeshttps://doi.org/10.1109/tfuzz.2026.3701309openalex-snapshot
A Novel Approach to Discover Fuzzy Frequent Georeferenced Patterns in Massive Georeferenced Temporal Quantitative Databases
inferred← citeshttps://doi.org/10.1109/tits.2023.3270334openalex-snapshot
Frequent Itemset-Driven Search for Finding Minimal Node Separators and its Application to Air Transportation Network Analysis
inferred← citeshttps://doi.org/10.1109/tkde.2021.3087343openalex-snapshot
Discovering Categorical Main and Interaction Effects Based on Association 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.2025.3579746openalex-snapshot
Mining Cross-Level High Utility Itemsets in Unstable and Negative Profit Databases
inferred← citeshttps://doi.org/10.1109/tkde.2026.3666851openalex-snapshot
DPHIM: Efficient Parallel Mining of High-Utility Itemsets on Multicore Processors and Its Evaluation
inferred← citeshttps://doi.org/10.1111/cgf.14926openalex-snapshot
Episodes and Topics in Multivariate Temporal Data
inferred← citeshttps://doi.org/10.1111/coin.12313openalex-snapshot
Opening the black box: Personalizing type 2 diabetes patients based on their latent phenotype and temporal associated complication rules
inferred← citeshttps://doi.org/10.1111/exsy.12881openalex-snapshot
A horizontal partitioning‐based method for frequent pattern mining in transport timetable
inferred← citeshttps://doi.org/10.1111/exsy.13464openalex-snapshot
A SCORPAN‐based data warehouse for digital soil mapping and association rule mining in support of sustainable agriculture and climate change analysis in the Maghreb region
inferred← citeshttps://doi.org/10.1111/exsy.70033openalex-snapshot
GRIMP : A Genetic Algorithm for Compression‐Based Descriptive Pattern Mining
inferred← citeshttps://doi.org/10.1142/s1752890925500096openalex-snapshot
Towards a New Method for Extracting Uncertain Data
inferred← citeshttps://doi.org/10.1145/3384544.3384563openalex-snapshot
How to Determine Minimum Support in Association Rule
inferred← citeshttps://doi.org/10.1145/3397536.3422206openalex-snapshot
Discovering Frequent Spatial Patterns in Very Large Spatiotemporal Databases
inferred← citeshttps://doi.org/10.1145/3462636openalex-snapshot
Mining High Utility Itemsets with Hill Climbing and Simulated Annealing
inferred← citeshttps://doi.org/10.1145/3472289openalex-snapshot
FPGA/GPU-based Acceleration for Frequent Itemsets Mining: A Comprehensive Review
inferred← citeshttps://doi.org/10.1145/3503161.3547770openalex-snapshot
Active Patterns Perceived for Stochastic Video Prediction
inferred← citeshttps://doi.org/10.1145/3511808.3557327openalex-snapshot
Frequent Itemset Mining with Local Differential Privacy
inferred← citeshttps://doi.org/10.1145/3524481.3527238openalex-snapshot
Testing non-testable programs using association rules
inferred← citeshttps://doi.org/10.1145/3531480openalex-snapshot
Smart System: Joint Utility and Frequency for Pattern Classification
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