On the public citation graph

Closed Multidimensional Sequential Pattern Mining

https://doi.org/10.1109/itng.2006.41
0 signed · 37 inferred · view as graph

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.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

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

inferredcites →https://doi.org/10.1007/3-540-49257-7_25openalex-snapshot
Discovering Frequent Closed Itemsets for Association Rules
inferredcites →https://doi.org/10.1007/bfb0100980openalex-snapshot
Pincer-search: A new algorithm for discovering the maximum frequent set
inferredcites →https://doi.org/10.1007/s10115-004-0175-4openalex-snapshot
TSP: Mining top-k closed sequential patterns
inferredcites →https://doi.org/10.1016/s0306-4379(99)00003-4openalex-snapshot
Efficient mining of association rules using closed itemset lattices
inferredcites →https://doi.org/10.1109/icde.2001.914830openalex-snapshot
PrefixSpan,: mining sequential patterns efficiently by prefix-projected pattern growth
inferredcites →https://doi.org/10.1109/icde.2003.1260845openalex-snapshot
Generalized closed itemsets for association rule mining
inferredcites →https://doi.org/10.1109/icde.2004.1319986openalex-snapshot
BIDE: efficient mining of frequent closed sequences
inferredcites →https://doi.org/10.1109/icdm.2002.1183905openalex-snapshot
Mining top-k frequent closed patterns without minimum support
inferredcites →https://doi.org/10.1109/icdm.2003.1250939openalex-snapshot
TSP: mining top-K closed sequential patterns
inferredcites →https://doi.org/10.1109/isic.2003.1254748openalex-snapshot
GRG: an efficient method for association rules mining on frequent closed itemsets
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/10.1137/1.9781611972733.15openalex-snapshot
CloSpan: Mining: Closed Sequential Patterns in Large Datasets
inferredcites →https://doi.org/10.1145/304181.304214openalex-snapshot
Bottom-up computation of sparse and Iceberg CUBE
inferredcites →https://doi.org/10.1145/304182.304214openalex-snapshot
Bottom-up computation of sparse and Iceberg CUBE
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/10.1145/502585.502600openalex-snapshot
Multi-dimensional sequential pattern mining
inferredcites →https://doi.org/10.1145/956750.956779openalex-snapshot
CLOSET+

What points at this work

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

inferred← citeshttps://doi.org/10.1007/978-0-387-87431-9_13openalex-snapshot
OLAP-Sequential Mining: Summarizing Trends from Historical
inferred← citeshttps://doi.org/10.1007/978-1-4419-0522-2_5openalex-snapshot
Mining Temporal Patterns to Improve Agents Behavior: Two Case Studies
inferred← citeshttps://doi.org/10.1007/978-3-540-88636-5_72openalex-snapshot
A Knowledge Discovery Framework for Learning Task Models from User Interactions in Intelligent Tutoring Systems
inferred← citeshttps://doi.org/10.1016/j.knosys.2010.08.002openalex-snapshot
Learning task models in ill-defined domain using an hybrid knowledge discovery framework
inferred← citeshttps://doi.org/10.1088/1757-899x/434/1/012042openalex-snapshot
The concept of sequential pattern mining for text
inferred← citeshttps://doi.org/10.1109/fskd.2007.265openalex-snapshot
Efficient Algorithms for Mining Closed Multidimensional Sequential Patterns
inferred← citeshttps://doi.org/10.1109/fskd.2014.6980910openalex-snapshot
Mining multi-attribute sequential pattern in onboard failure logging
inferred← citeshttps://doi.org/10.1109/icdmw.2008.25openalex-snapshot
Mining Correlated Pairs of Patterns in Multidimensional Structured Databases
inferred← citeshttps://doi.org/10.1109/icdmw.2008.82openalex-snapshot
An Efficient Sequential Pattern Mining Algorithm Based on the 2-Sequence Matrix
inferred← citeshttps://doi.org/10.1109/icfcse.2011.136openalex-snapshot
Mining of Association Model Pair in Multidimensional Structured Database
inferred← citeshttps://doi.org/10.1109/icices.2014.7033795openalex-snapshot
A survey on closed sequential pattern mining
inferred← citeshttps://doi.org/10.1109/tlt.2013.27openalex-snapshot
A multiparadigm intelligent tutoring system for robotic arm training
inferred← citeshttps://doi.org/10.3233/978-1-60750-028-5-383openalex-snapshot
Exploiting Partial Problem Spaces Learned from Users' Interactions to Provide Key Tutoring Services in Procedural and Ill-Defined Domains
inferred← citeshttps://doi.org/10.3934/aci.2023004openalex-snapshot
From basic approaches to novel challenges and applications in Sequential Pattern Mining
inferred← citeshttps://doi.org/10.4028/www.scientific.net/amr.268-270.1103openalex-snapshot
Top-K Strongly Related Projects Pairs Mining Algorithm Based on Threshold Estimate
inferred← citeshttps://doi.org/10.4156/jdcta.vol5.issue12.52openalex-snapshot
Exploring multi-dimensional sequential patterns across multi-dimensional multi-sequence databases
inferred← citeshttps://doi.org/10.48550/arxiv.0901.4761openalex-snapshot
A Knowledge Discovery Framework for Learning Task Models from User Interactions in Intelligent Tutoring Systems
inferred← citeshttps://doi.org/10.70675/4fb85f31za938z49f8za956z6c7b002ab6b5openalex-snapshot
Extraction de motifs fréquents sous contraintes
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.