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Tree partition based parallel frequent pattern mining on shared memory systems

https://doi.org/10.1109/ipdps.2006.1639620

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What this work says

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

inferredcites →https://doi.org/10.1007/3-540-36569-9_28openalex-snapshot

An Efficient Parallel and Distributed Algorithm for Counting Frequent Sets

inferredcites →https://doi.org/10.1007/3-540-48311-x_204openalex-snapshot

Mining of Association Rules in Very Large Databases: A Structured Parallel Approach⋆

inferredcites →https://doi.org/10.1007/978-3-540-45227-0_53openalex-snapshot

Tree Structure Based Parallel Frequent Pattern Mining on PC Cluster

inferredcites →https://doi.org/10.1109/icdm.2001.989600openalex-snapshot

Fast parallel association rule mining without candidacy generation

inferredcites →https://doi.org/10.1109/pdis.1996.568664openalex-snapshot

Hash based parallel algorithms for mining association rules

inferredcites →https://doi.org/10.1109/tkde.2005.18openalex-snapshot

Shared memory parallelization of data mining algorithms: techniques, programming interface, and performance

inferredcites →https://doi.org/10.1145/170035.170072openalex-snapshot

Mining association rules between sets of items in large databases

inferredcites →https://doi.org/10.1145/342009.335372openalex-snapshot

Mining frequent patterns without candidate generation

What points at this work

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

inferred← citeshttps://doi.org/10.1007/978-3-030-06222-4_6openalex-snapshot

Frequent Itemset Mining

inferred← citeshttps://doi.org/10.1007/978-3-642-15211-5_12openalex-snapshot

A Distributed Associative Classification Algorithm

inferred← citeshttps://doi.org/10.1007/s10586-024-04868-8openalex-snapshot

CrossFIM: a spark-based hybrid frequent itemset mining algorithm for large datasets

inferred← citeshttps://doi.org/10.1016/j.procs.2016.07.087openalex-snapshot

A Heuristic Rule Based Approximate Frequent Itemset Mining Algorithm

inferred← citeshttps://doi.org/10.1109/ccgrid.2019.00075openalex-snapshot

Pattern Mining from big IoT Data with fog Computing: Models, Issues, and Research Perspectives

inferred← citeshttps://doi.org/10.1109/chinacom.2013.6694632openalex-snapshot

A parallel algorithm of association rules based on cloud computing

inferred← citeshttps://doi.org/10.1109/datacom.2019.00053openalex-snapshot

Enumerating-Based Apriori Algorithms for Mining Frequent Patterns on MapReduce

inferred← citeshttps://doi.org/10.1109/hpcc.2009.37openalex-snapshot

Parallel and Distributed Frequent Pattern Mining in Large Databases

inferred← citeshttps://doi.org/10.1109/icecds.2017.8390164openalex-snapshot

ECLAT: Frequent itemset using MapReduce

inferred← citeshttps://doi.org/10.1109/iemre52042.2021.9386737openalex-snapshot

Application of Association Rule:Apriori Algorithm in E-Commerce

inferred← citeshttps://doi.org/10.1109/pdgc.2012.6449932openalex-snapshot

Periodic knowledge discovery through parallel paradigm

inferred← citeshttps://doi.org/10.1109/trustcom.2013.153openalex-snapshot

SeaRum: A Cloud-Based Service for Association Rule Mining

inferred← citeshttps://doi.org/10.1109/tsmc.2015.2437327openalex-snapshot

FiDoop: Parallel Mining of Frequent Itemsets Using MapReduce

inferred← citeshttps://doi.org/10.1186/s13015-016-0074-8openalex-snapshot

BicNET: Flexible module discovery in large-scale biological networks using biclustering

inferred← citeshttps://doi.org/10.17485/ijst/2015/v8i18/55109openalex-snapshot

A Improved Incremental and Interactive Frequent Pattern Mining Techniques for Market Basket Analysis and Fraud Detection in Distributed and Parallel Systems

inferred← citeshttps://doi.org/10.5120/ijca2017915068openalex-snapshot

A Survey on Achieving Best Knowledge from Frequent Item set Mining using Fidoop

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