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Mining Top-Rank-kErasable Itemsets by PID_lists

https://doi.org/10.1002/int.21580
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What this work says

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

inferredcites →https://doi.org/10.1016/j.knosys.2010.01.011openalex-snapshot
Text clustering using frequent itemsets
inferredcites →https://doi.org/10.1016/j.knosys.2010.07.007openalex-snapshot
Approximate weighted frequent pattern mining with/without noisy environments
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/10.1109/69.634757openalex-snapshot
Using a hash-based method with transaction trimming for mining association rules
inferredcites →https://doi.org/10.1109/icdm.2001.989541openalex-snapshot
CMAR: accurate and efficient classification based on multiple class-association rules
inferredcites →https://doi.org/10.1109/icmlc.2009.5212520openalex-snapshot
Mining erasable itemsets
inferredcites →https://doi.org/10.1109/tkde.2004.44openalex-snapshot
Mining frequent itemsets without support threshold: with and without item constraints
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.2005.183openalex-snapshot
MAFIA: a maximal frequent itemset algorithm
inferredcites →https://doi.org/10.1137/1.9781611972733.40openalex-snapshot
CPAR: Classification based on Predictive Association Rules
inferredcites →https://doi.org/10.1145/1066157.1066234openalex-snapshot
Mining top-K covering rule groups for gene expression data
inferredcites →https://doi.org/10.1145/1557019.1557039openalex-snapshot
Probabilistic frequent itemset mining in uncertain databases
inferredcites →https://doi.org/10.1145/1557019.1557097openalex-snapshot
Towards efficient mining of proportional fault-tolerant frequent itemsets
inferredcites →https://doi.org/10.1145/170036.170072openalex-snapshot
Mining association rules between sets of items in large databases
inferredcites →https://doi.org/10.1145/253262.253327openalex-snapshot
Beyond market baskets
inferredcites →https://doi.org/10.1145/335191.335372openalex-snapshot
Mining frequent patterns without candidate generation
inferredcites →https://doi.org/10.1145/335191.335376openalex-snapshot
Turbo-charging vertical mining of large databases
inferredcites →https://doi.org/10.1145/775047.775110openalex-snapshot
Frequent term-based text clustering
inferredcites →https://doi.org/10.1145/956750.956788openalex-snapshot
Fast vertical mining using diffsets
inferredcites →https://doi.org/10.21236/ada439469openalex-snapshot
HARMONY: Efficiently Mining the Best Rules for Classification
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.

inferred← citeshttps://doi.org/10.1002/widm.1137openalex-snapshot
A survey of erasable itemset mining algorithms
inferred← citeshttps://doi.org/10.1007/978-3-319-05476-6_8openalex-snapshot
A New Approach for Mining Top-Rank-k Erasable Itemsets
inferred← citeshttps://doi.org/10.1007/978-3-319-11289-3_52openalex-snapshot
Subsume Concept in Erasable Itemset Mining
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/10.1007/978-981-96-6008-7_6openalex-snapshot
Mining Erasable Patterns Using the Bitmap Method in Quantitative Component Databases
inferred← citeshttps://doi.org/10.1007/978-981-97-4982-9_24openalex-snapshot
Federated Erasable-Itemset Mining with Quasi-Erasable Itemsets
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/s10489-015-0748-9openalex-snapshot
An efficient algorithm for mining top-rank-k frequent patterns
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/10.1007/s10489-019-01622-1openalex-snapshot
Mining top-k frequent patterns from uncertain databases
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.engappai.2013.09.002openalex-snapshot
MEI: An efficient algorithm for mining erasable itemsets
inferred← citeshttps://doi.org/10.1016/j.engappai.2016.03.003openalex-snapshot
Erasable itemset mining over incremental databases with weight conditions
inferred← citeshttps://doi.org/10.1016/j.engappai.2017.09.010openalex-snapshot
Efficient algorithms for mining top-rank- k erasable patterns using pruning strategies and the subsume concept
inferred← citeshttps://doi.org/10.1016/j.engappai.2020.103735openalex-snapshot
Erasable pattern mining based on tree structures with damped window over data streams
inferred← citeshttps://doi.org/10.1016/j.eswa.2014.07.045openalex-snapshot
An efficient and effective algorithm for mining top-rank-k frequent patterns
inferred← citeshttps://doi.org/10.1016/j.eswa.2014.08.037openalex-snapshot
A fast perturbation algorithm using tree structure for privacy preserving utility mining
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.2019.01.034openalex-snapshot
Fast algorithms for mining maximal erasable patterns
inferred← citeshttps://doi.org/10.1016/j.eswa.2020.114530openalex-snapshot
Customized frequent patterns mining algorithms for enhanced Top-Rank-K frequent pattern mining
inferred← citeshttps://doi.org/10.1016/j.future.2015.12.012openalex-snapshot
Sliding window based weighted erasable stream pattern mining for stream data applications
inferred← citeshttps://doi.org/10.1016/j.knosys.2020.106064openalex-snapshot
Mining top-rank-k frequent weighted itemsets using WN-list structures and an early pruning strategy
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/bigdata.2017.8258125openalex-snapshot
Quasi-erasable itemset mining
inferred← citeshttps://doi.org/10.1109/inista.2017.8001172openalex-snapshot
An incremental mining algorithm for erasable itemsets
inferred← citeshttps://doi.org/10.1111/exsy.13122openalex-snapshot
ERIM : An ensemble of rare itemset mining and its application in the automotive industry
inferred← citeshttps://doi.org/10.1142/s2196888822500221openalex-snapshot
An Improving Approach for Top-Rank-k Frequent Pattern Mining
inferred← citeshttps://doi.org/10.1145/3227696.3227718openalex-snapshot
Maintenance of Erasable Itemsets for Product Deletion
inferred← citeshttps://doi.org/10.1145/3675669.3675698openalex-snapshot
Mining Erasable Itemsets from a Product Database with Quantitative Components
inferred← citeshttps://doi.org/10.17559/tv-20230719000815openalex-snapshot
A Parallel Mining Algorithm for Maximum Erasable Itemset Based on Multi-core Processor
inferred← citeshttps://doi.org/10.3233/ifs-141398openalex-snapshot
Mining weighted erasable patterns by using underestimated constraint-based pruning technique
inferred← citeshttps://doi.org/10.34248/bsengineering.1744061openalex-snapshot
Mining Top-K High Occupancy Itemsets
inferred← citeshttps://doi.org/10.5120/21613-4854openalex-snapshot
An Incremental Approach for Mining Erasable Itemsets
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