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FPGA/GPU-based Acceleration for Frequent Itemsets Mining: A Comprehensive 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.1232openalex-snapshot
A survey on graphic processing unit computing for large‐scale data mining
inferredcites →https://doi.org/10.1002/widm.1329openalex-snapshot
Frequent itemset mining: A 25 years review
inferredcites →https://doi.org/10.1007/11731139_85openalex-snapshot
Hardware Enhanced Mining for Association Rules
inferredcites →https://doi.org/10.1007/3-540-47887-6_34openalex-snapshot
Top Down FP-Growth for Association Rule Mining
inferredcites →https://doi.org/10.1007/978-0-387-47534-9_4openalex-snapshot
Frequent Pattern Mining in Data Streams
inferredcites →https://doi.org/10.1007/978-3-319-11897-0_7openalex-snapshot
Parallel Bees Swarm Optimization for Association Rules Mining Using GPU Architecture
inferredcites →https://doi.org/10.1007/978-3-319-39315-5_3openalex-snapshot
Frequent Itemsets Mining in Data Streams Using Reconfigurable Hardware
inferredcites →https://doi.org/10.1007/978-3-319-95651-0_5openalex-snapshot
Synergies Between Association Rules and Collaborative Filtering in Recommender System: An Application to Auto Industry
inferredcites →https://doi.org/10.1007/978-3-642-35567-7_27openalex-snapshot
Research on Parallel Association Rules Mining on GPU
inferredcites →https://doi.org/10.1007/978-3-662-48768-6_137openalex-snapshot
Effective FPGA-Based Enhancement of Quantitative Frequent Itemset Mining
inferredcites →https://doi.org/10.1007/978-81-322-2250-7_58openalex-snapshot
Parallel Implementation of FP Growth Algorithm on XML Data Using Multiple GPU
inferredcites →https://doi.org/10.1007/978-981-10-6487-6_8openalex-snapshot
An Hybrid Multi-Core/GPU-Based Mimetic Algorithm for Big Association Rule Mining
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/s10618-017-0501-6openalex-snapshot
Flexible constrained sampling with guarantees for pattern mining
inferredcites →https://doi.org/10.1007/s10844-017-0461-8openalex-snapshot
On the design of hardware-software architectures for frequent itemsets mining on data streams
inferredcites →https://doi.org/10.1007/s11227-011-0672-7openalex-snapshot
Parallel data mining techniques on Graphics Processing Unit with Compute Unified Device Architecture (CUDA)
inferredcites →https://doi.org/10.1007/s11227-013-0887-xopenalex-snapshot
Accelerating frequent itemset mining on graphics processing units
inferredcites →https://doi.org/10.1007/s11227-014-1366-8openalex-snapshot
GPU-based bees swarm optimization for association rules mining
inferredcites →https://doi.org/10.1007/s11227-016-1832-6openalex-snapshot
Semantic code clone detection for Internet of Things applications using reaching definition and liveness analysis
inferredcites →https://doi.org/10.1016/j.amc.2017.09.026openalex-snapshot
Parallel extraction of association rules from genomics data
inferredcites →https://doi.org/10.1016/j.bushor.2017.01.004openalex-snapshot
Big data: Dimensions, evolution, impacts, and challenges
inferredcites →https://doi.org/10.1016/j.compbiomed.2015.12.016openalex-snapshot
Mining frequent biological sequences based on bitmap without candidate sequence generation
inferredcites →https://doi.org/10.1016/j.dss.2004.09.007openalex-snapshot
Mining association rules with multiple minimum supports: a new mining algorithm and a support tuning mechanism
inferredcites →https://doi.org/10.1016/j.engappai.2014.12.008openalex-snapshot
Genetic programming and frequent itemset mining to identify feature selection patterns of iEEG and fMRI epilepsy data
inferredcites →https://doi.org/10.1016/j.eswa.2018.07.051openalex-snapshot
Emerging topic detection in twitter stream based on high utility pattern mining
inferredcites →https://doi.org/10.1016/j.eswa.2019.06.002openalex-snapshot
Combine clustering and frequent itemsets mining to enhance biomedical text summarization
inferredcites →https://doi.org/10.1016/j.eswa.2020.113440openalex-snapshot
On the design of hardware architectures for parallel frequent itemsets mining
inferredcites →https://doi.org/10.1016/j.ins.2018.01.046openalex-snapshot
GMiner: A fast GPU-based frequent itemset mining method for large-scale data
inferredcites →https://doi.org/10.1016/j.ins.2018.05.031openalex-snapshot
Mining diversified association rules in big datasets: A cluster/GPU/genetic approach
inferredcites →https://doi.org/10.1016/j.ins.2018.06.060openalex-snapshot
Exploiting GPU parallelism in improving bees swarm optimization for mining big transactional databases
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.is.2018.01.008openalex-snapshot
Influence-based approach to market basket analysis
inferredcites →https://doi.org/10.1016/j.jpdc.2018.11.002openalex-snapshot
Using hashing and lexicographic order for Frequent Itemsets Mining on data streams
inferredcites →https://doi.org/10.1016/j.knosys.2015.08.018openalex-snapshot
An uncertainty-based approach: Frequent itemset mining from uncertain data with different item importance
inferredcites →https://doi.org/10.1016/j.pisc.2015.11.013openalex-snapshot
High utility-itemset mining and privacy-preserving utility mining
inferredcites →https://doi.org/10.1016/j.procs.2012.04.010openalex-snapshot
Frequent Items Mining Acceleration Exploiting Fast Parallel Sorting on the GPU
inferredcites →https://doi.org/10.1108/ir.1999.04926fae.001openalex-snapshot
Lecture Notes in Computer Science 1205
inferredcites →https://doi.org/10.1109/69.846291openalex-snapshot
Scalable algorithms for association mining
inferredcites →https://doi.org/10.1109/access.2019.2949286openalex-snapshot
Black-Box vs. White-Box: Understanding Their Advantages and Weaknesses From a Practical Point of View
inferredcites →https://doi.org/10.1109/cluster.2011.61openalex-snapshot
GPApriori: GPU-Accelerated Frequent Itemset Mining
inferredcites →https://doi.org/10.1109/cyberc.2013.95openalex-snapshot
FPGA Acceleration for Intersection Computation in Frequent Itemset Mining
inferredcites →https://doi.org/10.1109/fccm.2005.31openalex-snapshot
Efficient Hardware Data Mining with the Apriori Algorithm on FPGAs
inferredcites →https://doi.org/10.1109/fccm.2006.22openalex-snapshot
An Architecture for Efficient Hardware Data Mining using Reconfigurable Computing Systems
inferredcites →https://doi.org/10.1109/icde.2010.5447856openalex-snapshot
FPGA acceleration for the frequent item problem
inferredcites →https://doi.org/10.1109/icdm.2003.1250944openalex-snapshot
Mining strong affinity association patterns in data sets with skewed support distribution
inferredcites →https://doi.org/10.1109/icdmw.2014.71openalex-snapshot
Parallel Frequent Pattern Mining without Candidate Generation on GPUs
inferredcites →https://doi.org/10.1109/icet.2009.5353149openalex-snapshot
Implementation of association rule mining using CUDA
inferredcites →https://doi.org/10.1109/ichpca.2014.7045323openalex-snapshot
Efficient implementation of apriori algorithm on HDFS using GPU
inferredcites →https://doi.org/10.1109/icsmc.2010.5641778openalex-snapshot
Parallel frequent patterns mining algorithm on GPU
inferredcites →https://doi.org/10.1109/ipdpsw.2016.98openalex-snapshot
Hardware Architectures for Frequent Itemset Mining Based on Equivalence Classes Partitioning
inferredcites →https://doi.org/10.1109/mc.2009.256openalex-snapshot
Data Mining for Software Engineering
inferredcites →https://doi.org/10.1109/reconfig.2008.80openalex-snapshot
A Reconfigurable Platform for Frequent Pattern Mining
inferredcites →https://doi.org/10.1109/reconfig.2013.6732297openalex-snapshot
Online heavy hitter detector on FPGA
inferredcites →https://doi.org/10.1109/tkde.2003.1161582openalex-snapshot
Alternative interest measures for mining associations in databases
inferredcites →https://doi.org/10.1109/tkde.2008.39openalex-snapshot
Hardware-Enhanced Association Rule Mining with Hashing and Pipelining
inferredcites →https://doi.org/10.1109/tkde.2010.216openalex-snapshot
Frequent Item Computation on a Chip
inferredcites →https://doi.org/10.1109/tkde.2018.2872587openalex-snapshot
Personalized Market Basket Prediction with Temporal Annotated Recurring Sequences
inferredcites →https://doi.org/10.1142/s0218488519500041openalex-snapshot
Finding Similar Documents Using Frequent Pattern Mining Methods
inferredcites →https://doi.org/10.1142/s0219649214500191openalex-snapshot
Mining Complex Spatial Patterns: Issues and Techniques
inferredcites →https://doi.org/10.1145/1132960.1132963openalex-snapshot
Interestingness measures for data mining
inferredcites →https://doi.org/10.1145/1166074.1166084openalex-snapshot
An integrated efficient solution for computing frequent and top- k elements in data streams
inferredcites →https://doi.org/10.1145/1565694.1565702openalex-snapshot
Frequent itemset mining on graphics processors
inferredcites →https://doi.org/10.1145/170036.170072openalex-snapshot
Mining association rules between sets of items in large databases
inferredcites →https://doi.org/10.1145/1835804.1835841openalex-snapshot
Mining uncertain data with probabilistic guarantees
inferredcites →https://doi.org/10.1145/2396761.2396874openalex-snapshot
GPU acceleration of probabilistic frequent itemset mining from uncertain databases
inferredcites →https://doi.org/10.1145/2457443.2457445openalex-snapshot
An FPGA-Based Accelerator for Frequent Itemset Mining
inferredcites →https://doi.org/10.1145/2554688.2554766openalex-snapshot
Accelerating frequent item counting with FPGA
inferredcites →https://doi.org/10.1145/2821508openalex-snapshot
A Real-Time FPGA-Based Accelerator for ECG Analysis and Diagnosis Using Association-Rule Mining
inferredcites →https://doi.org/10.1145/3027485openalex-snapshot
Efficient and Versatile FPGA Acceleration of Support Counting for Stream Mining of Sequences and Frequent Itemsets
inferredcites →https://doi.org/10.1145/312129.312191openalex-snapshot
Efficient mining of emerging patterns
inferredcites →https://doi.org/10.1145/312129.312274openalex-snapshot
Mining association rules with multiple minimum supports
inferredcites →https://doi.org/10.1145/3236024.3236032openalex-snapshot
NAR-miner: discovering negative association rules from code for bug detection
inferredcites →https://doi.org/10.1145/335191.335372openalex-snapshot
Mining frequent patterns without candidate generation
inferredcites →https://doi.org/10.1145/543613.543615openalex-snapshot
Models and issues in data stream systems
inferredcites →https://doi.org/10.1145/776985.776986openalex-snapshot
Issues in data stream management
inferredcites →https://doi.org/10.1145/956750.956807openalex-snapshot
Finding recent frequent itemsets adaptively over online data streams
inferredcites →https://doi.org/10.1177/0165551505050785openalex-snapshot
estWin: Online data stream mining of recent frequent itemsets by sliding window method
inferredcites →https://doi.org/10.1587/transinf.e97.d.779openalex-snapshot
Probabilistic Frequent Itemset Mining on a GPU Cluster
inferredcites →https://doi.org/10.2498/cit.1002361openalex-snapshot
Frequent Pattern-growth Algorithm on Multi-core CPU and GPU Processors
inferredcites →https://doi.org/10.5555/2872599.2872623openalex-snapshot
A self-adaptive method for frequent pattern mining using a CPU-GPU hybrid model
inferredcites →https://doi.org/10.5772/38445openalex-snapshot
Mining Frequent Itemsets over Recent Data Stream Based on Genetic Algorithm
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/s11042-022-13225-zopenalex-snapshot
Parallel frequent itemsets mining using distributed graphic processing units
inferred← citeshttps://doi.org/10.1016/j.asoc.2026.114661openalex-snapshot
Accelerating pattern mining on fuzzy data by packing truth values into blocks of bits
inferred← citeshttps://doi.org/10.1016/j.eswa.2024.123928openalex-snapshot
GMiner++: Boosting GPU-based frequent itemset mining by reducing redundant computations
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/10.1038/s41597-025-05195-2openalex-snapshot
Patterns Discovery Dataset for Particulate Matter (PM2.5) Pollution Trends in Japan
inferred← citeshttps://doi.org/10.1109/icpads67057.2025.11323079openalex-snapshot
FPGA-Based Accelerator for Parallel High Utility Itemset Mining Using Utility List
inferred← citeshttps://doi.org/10.1145/3617588openalex-snapshot
Landscape of High-Performance Python to Develop Data Science and Machine Learning Applications
inferred← citeshttps://doi.org/10.1155/2022/9607755openalex-snapshot
Construction of a Smart Supply Chain for Sand Factory Using the Edge-Computing-Based Deep Learning Algorithm
inferred← citeshttps://doi.org/10.1155/2024/4420578openalex-snapshot
Development and Implementation of an FPGA-Embedded Multimedia Remote Monitoring System for Information Technology Server Room Management
inferred← citeshttps://doi.org/10.11591/ijeecs.v28.i2.pp1012-1019openalex-snapshot
Matrix-matrix multiplication on graphics processing unit platform using tiling technique
inferred← citeshttps://doi.org/10.21203/rs.3.rs-2135830/v1openalex-snapshot
Performance Evaluation of Matrix-Matrix Multiplication using Parallel Programming Models on CPU Platforms
inferred← citeshttps://doi.org/10.3389/fpls.2023.1143326openalex-snapshot
Boosting precision crop protection towards agriculture 5.0 via machine learning and emerging technologies: A contextual review
inferred← citeshttps://doi.org/10.3390/electronics11152399openalex-snapshot
High-Level Design Optimizations for Implementing Data Stream Sketch Frequency Estimators on FPGAs
inferred← citeshttps://doi.org/10.3390/electronics12112376openalex-snapshot
Finding the Top-K Heavy Hitters in Data Streams: A Reconfigurable Accelerator Based on an FPGA-Optimized Algorithm
inferred← citeshttps://doi.org/10.3390/info15070372openalex-snapshot
Mining Spatial-Temporal Frequent Patterns of Natural Disasters in China Based on Textual Records
inferred← citeshttps://doi.org/10.3390/rs16193729openalex-snapshot
Aberration Modulation Correlation Method for Dim and Small Space Target Detection
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