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A comprehensive review on updating concept lattices and its application in updating association rules

https://doi.org/10.1002/widm.1401
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Outgoing claims: the papers this work cites, builds on, or argues with.

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A New Approach for Flexible Queries Using Fuzzy Ontologies
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Enhanced Algorithms for Fuzzy Formal Concepts Analysis
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Interestingnesslab: A Framework for Developing and Using Objective Interestingness Measures
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Making Use of Empty Intersections to Improve the Performance of CbO-Type Algorithms
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Machine Learning and Formal Concept Analysis
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A Unified View of Objective Interestingness Measures
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Lindig’s Algorithm for Concept Lattices over Graded Attributes
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Constructing Iceberg Lattices from Frequent Closures Using Generators
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Formal Concept Analysis
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Two Basic Algorithms in Concept Analysis
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Soft Concept Hierarchies to Summarise Data Streams and Highlight Anomalous Changes
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Mining Closed Itemsets in Data Stream Using Formal Concept Analysis
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What is a Fuzzy Concept Lattice? II
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Concept-Forming Operators on Multilattices
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Formal Concept Analysis
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Fuzzy-Begriffe
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Restructuring Lattice Theory: An Approach Based on Hierarchies of Concepts
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A Lattice Conceptual Clustering System and Its Application to Browsing Retrieval
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Attribute-incremental construction of the canonical implication basis
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A fast compound algorithm for mining generators, closed itemsets, and computing links between equivalence classes
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Efficiently mining frequent itemsets with weight and recency constraints
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GenMax: An Efficient Algorithm for Mining Maximal Frequent Itemsets
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Maximum entropy models and subjective interestingness: an application to tiles in binary databases
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Generating a Condensed Representation for Association Rules
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Research on Two Main Construction Methods of Concept Lattices
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Influence of dynamical changes on concept lattice and implication rules
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GALOIS : An order-theoretic approach to conceptual clustering
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Simultaneous mining of frequent closed itemsets and their generators: Foundation and algorithm
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Concept lattice reduction using fuzzy K-Means clustering
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DBV-Miner: A Dynamic Bit-Vector approach for fast mining frequent closed itemsets
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Formal concept analysis in knowledge processing: A survey on applications
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Incrementally building frequent closed itemset lattice
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A fast incremental algorithm for constructing concept lattices
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Incremental mining of weighted maximal frequent itemsets from dynamic databases
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An efficient dynamic superset bit-vector approach for mining frequent closed itemsets and their lattice structure
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Extracting compact and information lossless sets of fuzzy association rules
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Mining and updating association rules based on fuzzy concept lattice
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Ontology-based concept similarity in Formal Concept Analysis
inferredcites →https://doi.org/10.1016/j.ins.2011.09.023openalex-snapshot
Fast algorithm for computing fixpoints of Galois connections induced by object-attribute relational data
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Knowledge reduction in real decision formal contexts
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Concept learning via granular computing: A cognitive viewpoint
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Multi-cloud service composition using Formal Concept Analysis
inferredcites →https://doi.org/10.1016/j.knosys.2011.06.018openalex-snapshot
Semantic Web search based on rough sets and Fuzzy Formal Concept Analysis
inferredcites →https://doi.org/10.1016/j.knosys.2011.09.016openalex-snapshot
Research on domain ontology in different granulations based on concept lattice
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A lattice-based approach for mining most generalization association rules
inferredcites →https://doi.org/10.1016/j.knosys.2015.07.022openalex-snapshot
A fast incremental algorithm for deleting objects from a concept lattice
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An incremental algorithm to construct a lattice of set intersections
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Fuzzy sets
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A fast algorithm for building lattices
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Mining generalized association rules
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Computing iceberg concept lattices with Titanic
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Specification-Based Browsing of Software Component Libraries
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Formal concept analysis and linguistic hedges
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FUZZY CLUSTERING-BASED FORMAL CONCEPT ANALYSIS FOR ASSOCIATION RULES MINING
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Formal concept analysis for ontologies and their annotation files
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Mining traditional association rules using frequent itemsets lattice
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Application of Formal Concept Analysis in Association Rule Mining
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Selecting the right interestingness measure for association patterns
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Data mining: concepts and techniques

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