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Fast and memory efficient mining of frequent closed itemsets
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/3-540-45372-5_8openalex-snapshot
Approximation of Frequency Queries by Means of Free-Sets
inferredcites →https://doi.org/10.1007/3-540-45681-3_7openalex-snapshot
Mining All Non-derivable Frequent Itemsets
inferredcites →https://doi.org/10.1007/3-540-47887-6_15openalex-snapshot
Concise Representation of Frequent Patterns Based on Generalized Disjunction-Free Generators
inferredcites →https://doi.org/10.1007/3-540-49257-7_25openalex-snapshot
Discovering Frequent Closed Itemsets for Association Rules
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/10.1023/b:dami.0000040429.96086.c7openalex-snapshot
Mining Non-Redundant Association Rules
inferredcites →https://doi.org/10.1109/icdm.2001.989533openalex-snapshot
Concise representation of frequent patterns based on disjunction-free generators
inferredcites →https://doi.org/10.1109/icdm.2001.989550openalex-snapshot
H-mine: hyper-structure mining of frequent patterns in large databases
inferredcites →https://doi.org/10.1109/icdm.2002.1183921openalex-snapshot
Adaptive and resource-aware mining of frequent sets
inferredcites →https://doi.org/10.1109/tkde.2005.60openalex-snapshot
Efficient algorithms for mining closed itemsets and their lattice structure
inferredcites →https://doi.org/10.1137/1.9781611972726.27openalex-snapshot
CHARM: An Efficient Algorithm for Closed Itemset Mining
inferredcites →https://doi.org/10.1145/1007730.1007744openalex-snapshot
Advances in frequent itemset mining implementations
inferredcites →https://doi.org/10.1145/223784.223813openalex-snapshot
An effective hash-based algorithm for mining association rules
inferredcites →https://doi.org/10.1145/253260.253325openalex-snapshot
Dynamic itemset counting and implication rules for market basket data
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/10.1145/375551.375604openalex-snapshot
A condensed representation to find frequent patterns
inferredcites →https://doi.org/10.1145/380995.381017openalex-snapshot
Mining frequent patterns with counting inference
inferredcites →https://doi.org/10.1145/775047.775081openalex-snapshot
Mining frequent item sets by opportunistic projection
inferredcites →https://doi.org/10.1145/956750.956788openalex-snapshot
Fast vertical mining using diffsets
inferredcites →https://doi.org/10.1145/956755.956788openalex-snapshot
Fast vertical mining using diffsets
inferredcites →https://doi.org/10.1145/967900.968009openalex-snapshot
Statistical properties of transactional databases
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Using a Reinforced Concept Lattice to Incrementally Mine Association Rules from Closed Itemsets
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GC-Tree: A Fast Online Algorithm for Mining Frequent Closed Itemsets
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TGC-Tree: An Online Algorithm Tracing Closed Itemset and Transaction Set Simultaneously
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An Indexed Trie Approach to Incremental Mining of Closed Frequent Itemsets Based on a Galois Lattice Framework
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Maintenance algorithm for high average-utility itemsets with transaction deletion
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Efficient Tree Structures for High Utility Pattern Mining in Incremental Databases
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Efficient Mining of Large Maximal Bicliques from 3D Symmetric Adjacency Matrix
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Efficient Algorithms for Mining the Concise and Lossless Representation of High Utility Itemsets
inferred← citeshttps://doi.org/10.1109/tkde.2016.2592527openalex-snapshot
Scalable Daily Human Behavioral Pattern Mining from Multivariate Temporal Data
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Parallel Frequent Item Set Mining with Selective Item Replication
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A Fast Algorithm for Mining Closed Itemsets under the Length-Decreasing Support Constraint
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Share-Inherit: A novel approach for mining frequent patterns
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An Incremental Closed Frequent Itemsets Mining Algorithm Based on Shadow Prefix Tree
inferred← citeshttps://doi.org/10.1137/1.9781611972764.37openalex-snapshot
Mining frequent closed itemsets out of core
inferred← citeshttps://doi.org/10.1137/1.9781611972801.28openalex-snapshot
Robust mining of time intervals with semi-interval partial order patterns
inferred← citeshttps://doi.org/10.1142/s0129626413500126openalex-snapshot
MR-ARM: A MAP-REDUCE ASSOCIATION RULE MINING FRAMEWORK
inferred← citeshttps://doi.org/10.1142/s021821301250011xopenalex-snapshot
EFFICIENTLY MATCHING FREQUENT PATTERNS BASED ON BITMAP INVERTED FILES BUILT FROM CLOSED ITEMSETS
inferred← citeshttps://doi.org/10.1142/s0219622016500280openalex-snapshot
All in One: Mining Multiple Movement Patterns
inferred← citeshttps://doi.org/10.1145/1147234.1147248openalex-snapshot
Frequent closed itemset based algorithms
inferred← citeshttps://doi.org/10.1145/1341771.1341788openalex-snapshot
A Galois Lattice framework to handle updates in the mining of closed itemsets in dynamic databases
inferred← citeshttps://doi.org/10.1145/1517303.1517307openalex-snapshot
A framework for mining top-k frequent closed itemsets using order preserving generators
inferred← citeshttps://doi.org/10.1145/1816112.1816119openalex-snapshot
Margin-closed frequent sequential pattern mining
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Mining relaxed closed subspace clusters
inferred← citeshttps://doi.org/10.1145/2660859.2660955openalex-snapshot
Removal of Mirror Bicliques from a Symmetric Matrix
inferred← citeshttps://doi.org/10.1145/3709666openalex-snapshot
BCviz: A Linear-Space Index for Mining and Visualizing Cohesive Bipartite Subgraphs
inferred← citeshttps://doi.org/10.1155/2021/6653816openalex-snapshot
Mining Profitable and Concise Patterns in Large‐Scale Internet of Things Environments
inferred← citeshttps://doi.org/10.1155/2021/8841188openalex-snapshot
HOVA-FPPM: Flexible Periodic Pattern Mining in Time Series Databases Using Hashed Occurrence Vectors and Apriori Approach
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Towards Scalable Algorithm for Closed Itemset Mining in High-Dimensional Data
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EBPA: An efficient data structure for frequent closed itemset mining
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Maximum biclique search at billion scale
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Mining frequent closed itemsets from multidimensional databases
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Mining minimal non-redundant association rules using frequent itemsets lattice
inferred← citeshttps://doi.org/10.1504/ijista.2014.065181openalex-snapshot
An efficient approach for significant time intervals of frequent itemsets
inferred← citeshttps://doi.org/10.2139/ssrn.5812543openalex-snapshot
Association Rule Mining for Non-tabular Data
inferred← citeshttps://doi.org/10.21917/ijsc.2010.0010openalex-snapshot
PARALLEL MINING OF FREQUENT MAXIMAL ITEMSETS USING ORDER PRESERVING GENERATORS
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Mining frequent patterns using customer experience
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Performance of information retrieval models using term co-occurrences
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Finding Association Patterns of Disease Co-occurrence by using Closed Association Rule Generation
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Association Rules for Knowledge Discovery From E-News Articles: A Review of Apriori and FP-Growth Algorithms
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Temporal Mining for Distributed Systems
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The Association Rule of Corn Disease Symptoms by Using Frequent Pattern Growth and Random Forest
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Index-CloseMiner: An improved algorithm for mining frequent closed itemset
inferred← citeshttps://doi.org/10.3233/jifs-169128openalex-snapshot
A method for mining top-rank- k frequent closed itemsets
inferred← citeshttps://doi.org/10.3390/app8050769openalex-snapshot
Maintenance of Discovered High Average-Utility Itemsets in Dynamic Databases
inferred← citeshttps://doi.org/10.35940/ijeat.b4438.029320openalex-snapshot
Deriving Frequent Itemsets from Lossless Condensed Representation
inferred← citeshttps://doi.org/10.3724/sp.j.1016.2011.01589openalex-snapshot
Mining Hierarchical Concept Lattice for Faceted Navigation
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Depth-first search algorithm for mining frequent closed itemsets
inferred← citeshttps://doi.org/10.4000/msh.12081openalex-snapshot
Construction efficace du treillis des motifs fermés fréquents et extraction simultanée des bases génériques de règles
inferred← citeshttps://doi.org/10.4018/978-1-4666-1900-5.ch004openalex-snapshot
An Analytical Survey of Current Approaches to Mining Logical Rules from Data
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A Hybrid Algorithm of Mining Closed Itemsets for Large Databases
inferred← citeshttps://doi.org/10.4028/www.scientific.net/amm.263-266.231openalex-snapshot
Fast Mining of Closed Frequent Itemsets in Data Streams
inferred← citeshttps://doi.org/10.4028/www.scientific.net/amm.892.157openalex-snapshot
Improved BVBUC Algorithm to Discover Closed Itemsets in Long Biological Datasets
inferred← citeshttps://doi.org/10.4038/icter.v18i2.7297openalex-snapshot
Utilizing Association Rules in Knowledge Graphs for Enhanced News Summarization
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Mining Closed Itemsets: A Review
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RDB-MINER: A SQL-Based Algorithm for Mining True Relational Databases
inferred← citeshttps://doi.org/10.47839/ijc.8.3.691openalex-snapshot
PARALLEL MINING OF LARGE MAXIMAL BICLIQUES USING ORDER PRESERVING GENERATORS
inferred← citeshttps://doi.org/10.48550/arxiv.1312.1558openalex-snapshot
Efficient construction of the lattice of frequent closed patterns and simultaneous extraction of generic bases of rules
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