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An Efficient Load Balancing Multi-core Frequent Patterns Mining Algorithm

https://doi.org/10.1109/trustcom.2011.192
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Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

1 of the 17 references listed on this page is in the Retraction Watch database as retracted (export 2026-11-11): 10.1109/icct.2008.4716098 (notice 2008-12-16).

Dataset join, best-effort; this page does not check the publisher record. Retraction Watch data via Crossref Labs, used with credit; OpenCitations Index (CC0) and Meta (CC BY 4.0); dataset doi:10.7910/DVN/WT8VOA (CC BY 4.0).

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-540-68083-3_5openalex-snapshot
Tidset-Based Parallel FP-tree Algorithm for the Frequent Pattern Mining Problem on PC Clusters
inferredcites →https://doi.org/10.1007/978-3-540-73940-1_63openalex-snapshot
Load Balancing Approach Parallel Algorithm for Frequent Pattern Mining
inferredcites →https://doi.org/10.1016/j.eswa.2009.07.072openalex-snapshot
Parallel TID-based frequent pattern mining algorithm on a PC Cluster and grid computing system
inferredcites →https://doi.org/10.1016/j.eswa.2009.07.074openalex-snapshot
A load-balanced distributed parallel mining algorithm
inferredcites →https://doi.org/10.1016/j.jpdc.2009.04.008openalex-snapshot
Efficient parallel branch-and-bound algorithm for constructing minimum ultrametric trees
inferredcites →https://doi.org/10.1023/b:dami.0000040905.52966.1aopenalex-snapshot
Efficient Mining of Frequent Patterns Using Ascending Frequency Ordered Prefix-Tree
inferredcites →https://doi.org/10.1023/b:dapd.0000031634.19130.bdopenalex-snapshot
Frequent Pattern Mining on Message Passing Multiprocessor Systems
inferredcites →https://doi.org/10.1109/ccgrid.2009.83openalex-snapshot
Performance Issues in Parallelizing Data-Intensive Applications on a Multi-core Cluster
inferredcites →https://doi.org/10.1109/dasfaa.2003.1192369openalex-snapshot
Ascending frequency ordered prefix-tree: efficient mining of frequent patterns
inferredcites →https://doi.org/10.1109/icct.2008.4716098openalex-snapshot
Notice of Retraction: The research of improved apriori algorithm for mining association rules
inferredcites →https://doi.org/10.1109/icfcc.2010.5497596openalex-snapshot
AVI: Based on the vertical and intersection operation of the improved Apriori algorithm
inferredcites →https://doi.org/10.1109/icsmc.2010.5641778openalex-snapshot
Parallel frequent patterns mining algorithm on GPU
inferredcites →https://doi.org/10.1109/icsssm.2007.4280173openalex-snapshot
The Research of Improved Apriori Algorithm for Mining Association Rules
inferredcites →https://doi.org/10.1109/pdp.2009.43openalex-snapshot
Hybrid MPI/OpenMP Parallel Programming on Clusters of Multi-Core SMP Nodes
inferredcites →https://doi.org/10.1109/sera.2006.6openalex-snapshot
A Parallel Apriori Algorithm for Frequent Itemsets Mining
inferredcites →https://doi.org/10.1145/170035.170072openalex-snapshot
Mining association rules between sets of items in large databases
inferredcites →https://doi.org/10.3844/jcssp.2008.638.645openalex-snapshot
An Efficient Algorithm for Mining Maximal Frequent Item Sets

What points at this work

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

inferred← citeshttps://doi.org/10.1007/978-3-642-40820-5_21openalex-snapshot
Accelerating Parallel Frequent Itemset Mining on Graphics Processors with Sorting
inferred← citeshttps://doi.org/10.1007/s10489-016-0859-yopenalex-snapshot
An efficient method for mining frequent sequential patterns using multi-Core processors
inferred← citeshttps://doi.org/10.1016/j.asej.2021.05.019openalex-snapshot
Enhanced parallel mining algorithm for frequent sequential rules
inferred← citeshttps://doi.org/10.1016/j.engappai.2018.06.009openalex-snapshot
An efficient approach for mining sequential patterns using multiple threads on very large databases
inferred← citeshttps://doi.org/10.1016/j.eswa.2014.01.038openalex-snapshot
Efficient strategies for parallel mining class association rules
inferred← citeshttps://doi.org/10.1080/02522667.2017.1372143openalex-snapshot
TID based data and task parallelism for frequent data mining
inferred← citeshttps://doi.org/10.1109/access.2017.2739749openalex-snapshot
An Efficient Parallel Method for Mining Frequent Closed Sequential Patterns
inferred← citeshttps://doi.org/10.1109/access.2020.2992729openalex-snapshot
A Multi-Core Approach to Efficiently Mining High-Utility Itemsets in Dynamic Profit Databases
inferred← citeshttps://doi.org/10.1109/cisis.2013.114openalex-snapshot
Research on Task Allocation Strategy and Scheduling Algorithm of Multi-core Load Balance
inferred← citeshttps://doi.org/10.1109/cluster.2015.30openalex-snapshot
Large Scale Frequent Pattern Mining Using MPI One-Sided Model
inferred← citeshttps://doi.org/10.1109/hipc.2016.012openalex-snapshot
Fault Tolerant Frequent Pattern Mining
inferred← citeshttps://doi.org/10.4018/ijghpc.2015040106openalex-snapshot
Apriori-based High Efficiency Load Balancing Parallel Data Mining Algorithms on Multi-core Architectures
inferred← citeshttps://doi.org/10.5120/20215-2484openalex-snapshot
Intelligent Task Allocation in Multi Core Environment
inferred← citeshttps://doi.org/10.5815/ijitcs.2017.04.03openalex-snapshot
Improved Parallel Apriori Algorithm for Multi-cores
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