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Efficient Mining of Association Rules Based on Formal Concept Analysis

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What this work says

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

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Principles of Data Mining and Knowledge Discovery
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Approximation of Frequency Queries by Means of Free-Sets
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KI 2001: Advances in Artificial Intelligence
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Intelligent Structuring and Reducing of Association Rules with Formal Concept Analysis
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Frequent Closures as a Concise Representation for Binary Data Mining
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Mining All Non-derivable Frequent Itemsets
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Issues Raised by Three Years of Developing PJama: An Orthogonally Persistent Platform for Java™
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Discovering Frequent Closed Itemsets for Association Rules
inferredcites →https://doi.org/10.1007/3-540-62222-5_35openalex-snapshot
Methods and problems in data mining
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Adding structure to unstructured data
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Formal Concept Analysis for Knowledge Discovery and Data Mining: The New Challenges
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Finding all closed sets: A general approach
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Advances in Database Technology — EDBT'98
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Pincer-search: A new algorithm for discovering the maximum frequent set
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Generating a Condensed Representation for Association Rules
inferredcites →https://doi.org/10.1016/s0169-023x(02)00057-5openalex-snapshot
Computing iceberg concept lattices with Titanic
inferredcites →https://doi.org/10.1016/s0306-4379(99)00003-4openalex-snapshot
Efficient mining of association rules using closed itemset lattices
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Beyond Market Baskets: Generalizing Association Rules to Dependence Rules
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Lecture Notes in Computer Science 1205
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Data mining and knowledge discovery: making sense out of data
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Mining sequential patterns
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MAFIA: a maximal frequent itemset algorithm for transactional databases
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.2002.1183905openalex-snapshot
Mining top-k frequent closed patterns without minimum support
inferredcites →https://doi.org/10.1109/icdm.2004.10093openalex-snapshot
On Closed Constrained Frequent Pattern Mining
inferredcites →https://doi.org/10.1109/tkde.2005.60openalex-snapshot
Efficient algorithms for mining closed itemsets and their lattice structure
inferredcites →https://doi.org/10.1137/1.9781611972757.22openalex-snapshot
Mining Non-Derivable Association Rules
inferredcites →https://doi.org/10.1145/1282480openalex-snapshot
Proceedings of the 1st International Conference on Very Large Data Bases - VLDB '75
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Mining association rules between sets of items in large databases
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/10.1145/253260.253327openalex-snapshot
Beyond market baskets
inferredcites →https://doi.org/10.1145/276304.276313openalex-snapshot
Efficiently mining long patterns from databases
inferredcites →https://doi.org/10.1145/335191.335372openalex-snapshot
Mining frequent patterns without candidate generation
inferredcites →https://doi.org/10.1145/347090.347101openalex-snapshot
Generating non-redundant association rules
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
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CLOSET+
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BAMBOO: Accelerating Closed Itemset Mining by Deeply Pushing the Length-Decreasing Support Constraint
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Journal of Universal Computer Science
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Galois Connections and Data Mining.
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Data mining: concepts and techniques

What points at this work

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

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