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Mining frequent itemsets without support threshold: with and without item constraints
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/3-540-39963-1_7openalex-snapshot
Mining N-most Interesting Itemsets
inferredcites →https://doi.org/10.1007/3-540-49257-7_25openalex-snapshot
Discovering Frequent Closed Itemsets for Association Rules
inferredcites →https://doi.org/10.1023/a:1009891813863openalex-snapshot
Scalable Techniques for Mining Causal Structures
inferredcites →https://doi.org/10.1023/b:dami.0000023674.74932.4copenalex-snapshot
Pushing Convertible Constraints in Frequent Itemset Mining
inferredcites →https://doi.org/10.1109/icde.1997.581756openalex-snapshot
Clustering association rules
inferredcites →https://doi.org/10.1109/icde.2000.839450openalex-snapshot
Efficient mining of constrained correlated sets
inferredcites →https://doi.org/10.1109/icde.2001.914856openalex-snapshot
Mining frequent itemsets with convertible constraints
inferredcites →https://doi.org/10.1117/12.460253openalex-snapshot
FP-tree approach for mining N -most interesting itemsets
inferredcites →https://doi.org/10.1145/191246.191314openalex-snapshot
Finding interesting rules from large sets of discovered association rules
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/10.1145/276304.276314openalex-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/10.1145/335191.335376openalex-snapshot
Turbo-charging vertical mining of large databases
inferredcites →https://doi.org/10.1145/342009.335372openalex-snapshot
Mining frequent patterns without candidate generation
inferredcites →https://doi.org/10.1145/342009.335376openalex-snapshot
Turbo-charging vertical mining of large databases
inferredcites →https://doi.org/10.1145/502512.502572openalex-snapshot
Real world performance of association rule algorithms
inferredcites →https://doi.org/10.1145/568271.223813openalex-snapshot
An effective hash-based algorithm for mining association rules
inferredcites →https://doi.org/10.1145/568574.568580openalex-snapshot
Constrained frequent pattern mining
inferredcites →https://doi.org/10.1145/568574.568581openalex-snapshot
Exploiting succinct constraints using FP-trees
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Mining Top-Rank-kErasable Itemsets by PID_lists
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Frequent Pattern Mining with Preferences–Utility Functions Approach
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On Exploring the Power-Law Relationship in the Itemset Support Distribution
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Mining Interesting Rules Without Support Requirement: A General Universal Existential Upward Closure Property
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Improved Algorithm for Mining N-Most Interesting Itemsets
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Efficient Discovery of Association Rules and Frequent Itemsets through Sampling with Tight Performance Guarantees
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Min-Max Itemset Trees for Dense and Categorical Datasets
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TKAR: Efficient Mining of Top-k Association Rules on Real—Life Datasets
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Discovering Frequent Patterns in Very Large Transactional Databases
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Extracting Representative Co-location Patterns Considering Distributions of Spatial Features and Instances
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Pushing multiple convertible constrains into frequent itemsets mining
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Power-law relationship and self-similarity in the itemset support distribution: analysis and applications
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