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A survey of erasable itemset mining algorithms
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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/int.21580openalex-snapshot
Mining Top-Rank-kErasable Itemsets by PID_lists
inferredcites →https://doi.org/10.1002/widm.1086openalex-snapshot
Incremental association rule mining: a survey
inferredcites →https://doi.org/10.1007/978-3-319-02821-7_27openalex-snapshot
Maintenance of a Frequent-Itemset Lattice Based on Pre-large Concept
inferredcites →https://doi.org/10.1007/978-3-319-05476-6_8openalex-snapshot
A New Approach for Mining Top-Rank-k Erasable Itemsets
inferredcites →https://doi.org/10.1007/978-3-642-17316-5_20openalex-snapshot
An Efficient Algorithm for Mining Erasable Itemsets
inferredcites →https://doi.org/10.1007/s00354-008-0072-6openalex-snapshot
Using the Structure of Prelarge Trees to Incrementally Mine Frequent Itemsets
inferredcites →https://doi.org/10.1007/s10489-014-0551-zopenalex-snapshot
An effective approach for maintenance of pre-large-based frequent-itemset lattice in incremental mining
inferredcites →https://doi.org/10.1007/s11432-012-4638-zopenalex-snapshot
A new algorithm for fast mining frequent itemsets using N-lists
inferredcites →https://doi.org/10.1016/j.csda.2009.01.015openalex-snapshot
Maintenance of fast updated frequent pattern trees for record deletion
inferredcites →https://doi.org/10.1016/j.engappai.2012.01.017openalex-snapshot
Automatic detection of erythemato-squamous diseases using PSO–SVM based on association rules
inferredcites →https://doi.org/10.1016/j.engappai.2012.02.003openalex-snapshot
A service scenario generation scheme based on association rule mining for elderly surveillance system in a smart home environment
inferredcites →https://doi.org/10.1016/j.engappai.2013.09.002openalex-snapshot
MEI: An efficient algorithm for mining erasable itemsets
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.2010.10.047openalex-snapshot
An improved frequent pattern growth method for mining association rules
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.2011.03.042openalex-snapshot
Interestingness measures for association rules: Combination between lattice and hash tables
inferredcites →https://doi.org/10.1016/j.eswa.2011.09.087openalex-snapshot
A new mining approach for uncertain databases using CUFP trees
inferredcites →https://doi.org/10.1016/j.eswa.2011.09.143openalex-snapshot
Fast mining erasable itemsets using NC_sets
inferredcites →https://doi.org/10.1016/j.eswa.2012.01.062openalex-snapshot
DBV-Miner: A Dynamic Bit-Vector approach for fast mining frequent closed itemsets
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.2012.03.036openalex-snapshot
Classification based on association rules: A lattice-based approach
inferredcites →https://doi.org/10.1016/j.eswa.2012.08.065openalex-snapshot
A new method for mining Frequent Weighted Itemsets based on WIT-trees
inferredcites →https://doi.org/10.1016/j.eswa.2012.10.035openalex-snapshot
CAR-Miner: An efficient algorithm for mining class-association rules
inferredcites →https://doi.org/10.1016/j.ins.2014.03.023openalex-snapshot
Maintenance of prelarge trees for data mining with modified records
inferredcites →https://doi.org/10.1016/j.knosys.2006.08.005openalex-snapshot
BitTableFI: An efficient mining frequent itemsets algorithm
inferredcites →https://doi.org/10.1016/j.knosys.2008.03.011openalex-snapshot
Index-BitTableFI: An improved algorithm for mining frequent itemsets
inferredcites →https://doi.org/10.1016/j.knosys.2012.02.002openalex-snapshot
An efficient mining algorithm for maximal weighted frequent patterns in transactional databases
inferredcites →https://doi.org/10.1016/j.knosys.2013.02.003openalex-snapshot
A lattice-based approach for mining most generalization association rules
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/10.1080/18756891.2010.9727736openalex-snapshot
A New Fast Vertical Method for Mining Frequent Patterns
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/smc.2013.388openalex-snapshot
An Efficient Algorithm for Mining Erasable Itemsets Using the Difference of NC-Sets
inferredcites →https://doi.org/10.1109/smc.2013.791openalex-snapshot
A Hybrid Approach for Mining Frequent Itemsets
inferredcites →https://doi.org/10.1109/tkde.2005.166openalex-snapshot
Fast algorithms for frequent itemset mining using FP-trees
inferredcites →https://doi.org/10.1109/tkde.2005.60openalex-snapshot
Efficient algorithms for mining closed itemsets and their lattice structure
inferredcites →https://doi.org/10.1109/tkde.2006.10openalex-snapshot
Fast and memory efficient mining of frequent closed itemsets
inferredcites →https://doi.org/10.1109/tkde.2008.190openalex-snapshot
A Survey of Uncertain Data Algorithms and Applications
inferredcites →https://doi.org/10.1109/tkde.2011.165openalex-snapshot
Efficient Mining of Frequent Item Sets on Large Uncertain Databases
inferredcites →https://doi.org/10.1145/1401890.1401922openalex-snapshot
Direct mining of discriminative and essential frequent patterns via model-based search tree
inferredcites →https://doi.org/10.1145/1401890.1401930openalex-snapshot
Quantitative evaluation of approximate frequent pattern mining algorithms
inferredcites →https://doi.org/10.1145/1557019.1557030openalex-snapshot
Frequent pattern mining with uncertain data
inferredcites →https://doi.org/10.1145/1557019.1557097openalex-snapshot
Towards efficient mining of proportional fault-tolerant frequent itemsets
inferredcites →https://doi.org/10.1145/170035.170072openalex-snapshot
Mining association rules between sets of items in large databases
inferredcites →https://doi.org/10.1145/170036.170072openalex-snapshot
Mining association rules between sets of items in large databases
inferredcites →https://doi.org/10.1145/276304.276314openalex-snapshot
Automatic subspace clustering of high dimensional data for data mining applications
inferredcites →https://doi.org/10.1145/276305.276314openalex-snapshot
Automatic subspace clustering of high dimensional data for data mining applications
inferredcites →https://doi.org/10.1145/335191.335372openalex-snapshot
Mining frequent patterns without candidate generation
inferredcites →https://doi.org/10.1145/342009.335372openalex-snapshot
Mining frequent patterns without candidate generation
inferredcites →https://doi.org/10.1145/956750.956788openalex-snapshot
Fast vertical mining using diffsets
inferredcites →https://doi.org/10.2991/ijcis.2010.3.6.4openalex-snapshot
A New Fast Vertical Method for Mining Frequent Patterns
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1002/widm.1181openalex-snapshot
The lattice‐based approaches for mining association rules: a review
inferred← citeshttps://doi.org/10.1002/widm.1307openalex-snapshot
A survey on association rules mining using heuristics
inferred← citeshttps://doi.org/10.1007/978-3-319-15702-3_36openalex-snapshot
Discovering Erasable Closed Patterns
inferred← citeshttps://doi.org/10.1007/978-981-15-0199-9_16openalex-snapshot
Mining of Removable Closed Patterns in Goods Dataset
inferred← citeshttps://doi.org/10.1007/978-981-99-0105-0_33openalex-snapshot
Multiple-Threshold Erasable Mining Under the Tightest Constraint
inferred← citeshttps://doi.org/10.1007/s10489-014-0644-8openalex-snapshot
EIFDD: An efficient approach for erasable itemset mining of very dense datasets
inferred← citeshttps://doi.org/10.1007/s10489-018-1280-5openalex-snapshot
SPPC: a new tree structure for mining erasable patterns in data streams
inferred← citeshttps://doi.org/10.1016/j.asoc.2016.01.010openalex-snapshot
DiffNodesets: An efficient structure for fast mining frequent itemsets
inferred← citeshttps://doi.org/10.1016/j.eswa.2015.04.048openalex-snapshot
An N-list-based algorithm for mining frequent closed patterns
inferred← citeshttps://doi.org/10.1016/j.eswa.2016.10.028openalex-snapshot
Mining erasable itemsets with subset and superset itemset constraints
inferred← citeshttps://doi.org/10.1016/j.eswa.2018.03.041openalex-snapshot
negFIN: An efficient algorithm for fast mining frequent itemsets
inferred← citeshttps://doi.org/10.1016/j.eswa.2019.01.034openalex-snapshot
Fast algorithms for mining maximal erasable patterns
inferred← citeshttps://doi.org/10.1109/access.2017.2676803openalex-snapshot
Efficient Algorithms for Mining Erasable Closed Patterns From Product Datasets
inferred← citeshttps://doi.org/10.1109/inista.2017.8001172openalex-snapshot
An incremental mining algorithm for erasable itemsets
inferred← citeshttps://doi.org/10.47164/ijngc.v13i3.806openalex-snapshot
Enhancing the Performance of Association Rule Generation over Dynamic Data using Incremental Tree Structures
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