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On the discovery of association rules by means of evolutionary algorithms

https://doi.org/10.1002/widm.18
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What this work says

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

inferredcites →https://doi.org/10.1007/3-540-47887-6_5openalex-snapshot
Discovering Numeric Association Rules via Evolutionary Algorithm
inferredcites →https://doi.org/10.1007/3-540-56602-3_142openalex-snapshot
SIA: A supervised inductive algorithm with genetic search for learning attributes based concepts
inferredcites →https://doi.org/10.1007/978-0-387-09823-4openalex-snapshot
Data Mining and Knowledge Discovery Handbook
inferredcites →https://doi.org/10.1007/978-0-387-36797-2openalex-snapshot
Evolutionary Algorithms for Solving Multi-Objective Problems
inferredcites →https://doi.org/10.1007/978-1-4757-3643-4openalex-snapshot
The Design of Innovation
inferredcites →https://doi.org/10.1007/978-1-4757-5184-0openalex-snapshot
Evolutionary Algorithms for Solving Multi-Objective Problems
inferredcites →https://doi.org/10.1007/978-3-540-30133-2_35openalex-snapshot
Novelty in the Generation of Initial Population for Genetic Algorithms
inferredcites →https://doi.org/10.1007/978-3-540-48765-4_18openalex-snapshot
A Genetic Algorithm for Visualizing Networks of Association Rules
inferredcites →https://doi.org/10.1007/978-3-540-77467-9openalex-snapshot
Multi-Objective Evolutionary Algorithms for Knowledge Discovery from Databases
inferredcites →https://doi.org/10.1007/978-3-540-77467-9_3openalex-snapshot
Evolutionary Multi-objective Rule Selection for Classification Rule Mining
inferredcites →https://doi.org/10.1007/978-3-642-00193-2_4openalex-snapshot
Dynamic Association Rule Mining Using Genetic Algorithms
inferredcites →https://doi.org/10.1007/978-3-642-05173-9_15openalex-snapshot
Survey on Association Rules Mining Algorithms
inferredcites →https://doi.org/10.1007/978-3-642-14478-3_60openalex-snapshot
Global Search Analysis of Spatial Gene Expression Data Using Genetic Algorithm
inferredcites →https://doi.org/10.1007/978-3-662-04923-5openalex-snapshot
Data Mining and Knowledge Discovery with Evolutionary Algorithms
inferredcites →https://doi.org/10.1007/978-3-662-05094-1openalex-snapshot
Introduction to Evolutionary Computing
inferredcites →https://doi.org/10.1007/978-3-7091-6230-9_65openalex-snapshot
Mining Numeric Association Rules with Genetic Algorithms
inferredcites →https://doi.org/10.1007/bf00993044openalex-snapshot
Competition-based induction of decision models from examples
inferredcites →https://doi.org/10.1007/bf02706249openalex-snapshot
Interestingness of association rules in data mining: Issues relevant to e-commerce
inferredcites →https://doi.org/10.1007/bf02828242openalex-snapshot
Association rules applied to intrusion detection
inferredcites →https://doi.org/10.1007/s00500-005-0476-xopenalex-snapshot
An efficient genetic algorithm for automated mining of both positive and negative quantitative association rules
inferredcites →https://doi.org/10.1007/s00500-005-0509-5openalex-snapshot
Multi-objective genetic algorithm based approaches for mining optimized fuzzy association rules
inferredcites →https://doi.org/10.1007/s00500-006-0046-xopenalex-snapshot
A GA-based Fuzzy Mining Approach to Achieve a Trade-off Between Number of Rules and Suitability of Membership Functions
inferredcites →https://doi.org/10.1007/s00500-008-0284-1openalex-snapshot
Rough particle swarm optimization and its applications in data mining
inferredcites →https://doi.org/10.1007/s00500-008-0373-1openalex-snapshot
Evolutionary and metaheuristics based data mining
inferredcites →https://doi.org/10.1007/s10044-007-0090-xopenalex-snapshot
Extraction and optimization of fuzzy association rules using multi-objective genetic algorithm
inferredcites →https://doi.org/10.1007/s10479-005-3971-7openalex-snapshot
Metaheuristics in Combinatorial Optimization
inferredcites →https://doi.org/10.1007/s10489-006-6925-0openalex-snapshot
Utilizing Genetic Algorithms to Optimize Membership Functions for Fuzzy Weighted Association Rules Mining
inferredcites →https://doi.org/10.1007/s12065-007-0001-5openalex-snapshot
Genetic fuzzy systems: taxonomy, current research trends and prospects
inferredcites →https://doi.org/10.1016/0004-3702(89)90050-7openalex-snapshot
Classifier systems and genetic algorithms
inferredcites →https://doi.org/10.1016/j.artmed.2009.03.004openalex-snapshot
Exploring ant-based algorithms for gene expression data analysis
inferredcites →https://doi.org/10.1016/j.asoc.2007.05.003openalex-snapshot
MODENAR: Multi-objective differential evolution algorithm for mining numeric association rules
inferredcites →https://doi.org/10.1016/j.asoc.2009.11.023openalex-snapshot
Application of particle swarm optimization to association rule mining
inferredcites →https://doi.org/10.1016/j.chaos.2008.04.024openalex-snapshot
Chaotically encoded particle swarm optimization algorithm and its applications
inferredcites →https://doi.org/10.1016/j.eswa.2006.08.035openalex-snapshot
Mining association rules through integration of clustering analysis and ant colony system for health insurance database in Taiwan
inferredcites →https://doi.org/10.1016/j.eswa.2008.01.028openalex-snapshot
Genetic algorithm-based strategy for identifying association rules without specifying actual minimum support
inferredcites →https://doi.org/10.1016/j.eswa.2009.01.067openalex-snapshot
An improved approach to find membership functions and multiple minimum supports in fuzzy data mining
inferredcites →https://doi.org/10.1016/j.fss.2004.09.014openalex-snapshot
Genetic algorithm based framework for mining fuzzy association rules
inferredcites →https://doi.org/10.1016/j.fss.2008.05.012openalex-snapshot
Learning the membership function contexts for mining fuzzy association rules by using genetic algorithms
inferredcites →https://doi.org/10.1016/j.ins.2003.03.021openalex-snapshot
Multi-objective rule mining using genetic algorithms
inferredcites →https://doi.org/10.1016/j.knosys.2005.11.001openalex-snapshot
Determining membership functions and minimum fuzzy support in finding fuzzy association rules for classification problems
inferredcites →https://doi.org/10.1016/s0165-0114(03)00114-3openalex-snapshot
Fuzzy rule selection by multi-objective genetic local search algorithms and rule evaluation measures in data mining
inferredcites →https://doi.org/10.1023/a:1022812808206openalex-snapshot
A Statistical Theory for Quantitative Association Rules
inferredcites →https://doi.org/10.1023/b:dami.0000005258.31418.83openalex-snapshot
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
inferredcites →https://doi.org/10.1093/bioinformatics/btn490openalex-snapshot
GenMiner: mining non-redundant association rules from integrated gene expression data and annotations
inferredcites →https://doi.org/10.1093/oso/9780195131581.001.0001openalex-snapshot
Swarm Intelligence
inferredcites →https://doi.org/10.1109/4235.996017openalex-snapshot
A fast and elitist multiobjective genetic algorithm: NSGA-II
inferredcites →https://doi.org/10.1109/69.846291openalex-snapshot
Scalable algorithms for association mining
inferredcites →https://doi.org/10.1109/cec.2004.1331124openalex-snapshot
A multicriteria genetic algorithm to analyze microarray data
inferredcites →https://doi.org/10.1109/cec9537.2005openalex-snapshot
2005 IEEE Congress on Evolutionary Computation
inferredcites →https://doi.org/10.1109/cis.workshops.2007.22openalex-snapshot
Association Rules Mining Using Multi-objective Coevolutionary Algorithm
inferredcites →https://doi.org/10.1109/fuzzy.2009.5277342openalex-snapshot
Speeding up genetic-fuzzy mining by fuzzy clustering
inferredcites →https://doi.org/10.1109/icccas.2009.5250446openalex-snapshot
Mining gene network by combined association rules and genetic algorithm
inferredcites →https://doi.org/10.1109/iccea.2004.1459313openalex-snapshot
Association rules data mining in manufacturing information system based on genetic algorithms
inferredcites →https://doi.org/10.1109/icdm.2004.10050openalex-snapshot
Integrating Multi-Objective Genetic Algorithms into Clustering for Fuzzy Association Rules Mining
inferredcites →https://doi.org/10.1109/icec.1998.699146openalex-snapshot
A modified particle swarm optimizer
inferredcites →https://doi.org/10.1109/tevc.2007.900992openalex-snapshot
Genetic-Fuzzy Data Mining With Divide-and-Conquer Strategy
inferredcites →https://doi.org/10.1109/tevc.2009.2039140openalex-snapshot
Genetics-Based Machine Learning for Rule Induction: State of the Art, Taxonomy, and Comparative Study
inferredcites →https://doi.org/10.1109/tfuzz.2004.840130openalex-snapshot
On the representation, measurement, and discovery of fuzzy associations
inferredcites →https://doi.org/10.1109/tfuzz.2007.903327openalex-snapshot
Cluster-Based Evaluation in Fuzzy-Genetic Data Mining
inferredcites →https://doi.org/10.1109/tsmcb.2002.1018771openalex-snapshot
COR: a methodology to improve ad hoc data-driven linguistic rule learning methods by inducing cooperation among rules
inferredcites →https://doi.org/10.1109/wicom.2008.2508openalex-snapshot
Genetic Algorithm-Based Rules Discovery for Networked Manufacturing Resources Management
inferredcites →https://doi.org/10.1109/wise.2000.882868openalex-snapshot
Online Web mining transactions association rules using frame metadata model
inferredcites →https://doi.org/10.1142/4177openalex-snapshot
Genetic Fuzzy Systems
inferredcites →https://doi.org/10.1142/4177#t=tocopenalex-snapshot
Genetic Fuzzy Systems: Evolutionary Tuning And Learning Of Fuzzy Knowledge Bases
inferredcites →https://doi.org/10.1145/1388969.1389017openalex-snapshot
First approach toward on-line evolution of association rules with learning classifier systems
inferredcites →https://doi.org/10.1145/170035.170072openalex-snapshot
Mining association rules between sets of items in large databases
inferredcites →https://doi.org/10.1145/253260.253325openalex-snapshot
Dynamic itemset counting and implication rules for market basket data
inferredcites →https://doi.org/10.1145/502512.502569openalex-snapshot
Discovering associations with numeric variables
inferredcites →https://doi.org/10.1145/937503.937505openalex-snapshot
Metaheuristics in combinatorial optimization
inferredcites →https://doi.org/10.1162/106365603322365289openalex-snapshot
Accuracy-Based Learning Classifier Systems: Models, Analysis and Applications to Classification Tasks
inferredcites →https://doi.org/10.1201/9781420010749-33openalex-snapshot
Ant Colony Optimization
inferredcites →https://doi.org/10.17951/ai.2006.4.1.204-217openalex-snapshot
Discovery of association rules from medical data -classical and evolutionary approaches
inferredcites →https://doi.org/10.25102/fer.2004.02.01openalex-snapshot
FUZZY ASSOCIATION RULES FOR ESTIMATING CONSUMER BEHAVIOUR MODELS AND THEIR APPLICATION TO EXPLAINING TRUST IN INTERNET SHOPPING
inferredcites →https://doi.org/10.3233/ida-2005-9503openalex-snapshot
Dynamic Association Rule Mining using Genetic Algorithms
inferredcites →https://doi.org/10.3844/ajassp.2006.2086.2095openalex-snapshot
Multi-objective Genetic Algorithm for Association Rule Mining Using a Homogeneous Dedicated Cluster of Workstations
inferredcites →https://doi.org/10.4135/9781412973380.n22openalex-snapshot
An Introduction to Data Mining
inferredcites →https://doi.org/10.4324/9780080878096-12openalex-snapshot
Introduction to Data Mining
inferredcites →https://doi.org/10.5555/140629.140633openalex-snapshot
Knowledge discovery in databases
inferredcites →https://doi.org/10.5555/1760894.1760937openalex-snapshot
Evolutionary approach for mining association rules on dynamic databases
inferredcites →https://doi.org/10.7551/mitpress/1290.001.0001openalex-snapshot
Ant Colony Optimization

What points at this work

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

inferred← citeshttps://doi.org/10.1002/widm.1086openalex-snapshot
Incremental association rule mining: a survey
inferred← citeshttps://doi.org/10.1002/widm.1176openalex-snapshot
Fuzzy frameworks for mining data associations: fuzzy association rules and beyond
inferred← citeshttps://doi.org/10.1002/widm.1307openalex-snapshot
A survey on association rules mining using heuristics
inferred← citeshttps://doi.org/10.1007/978-3-031-29313-9_32openalex-snapshot
Heuristic Algorithm for Extracting Frequent Patterns in Transactional Databases
inferred← citeshttps://doi.org/10.1007/978-3-319-24834-9_12openalex-snapshot
A Bacterial Colony Algorithm for Association Rule Mining
inferred← citeshttps://doi.org/10.1007/978-3-319-26535-3_12openalex-snapshot
Discovery of Interesting Association Rules Using Genetic Algorithm with Adaptive Mutation
inferred← citeshttps://doi.org/10.1007/978-3-319-26535-3_16openalex-snapshot
A New Evolutionary Algorithm for Extracting a Reduced Set of Interesting Association Rules
inferred← citeshttps://doi.org/10.1007/978-3-319-33858-3_4openalex-snapshot
Pattern Mining with Genetic Algorithms
inferred← citeshttps://doi.org/10.1007/978-3-319-33858-3_5openalex-snapshot
Genetic Programming in Pattern Mining
inferred← citeshttps://doi.org/10.1007/978-3-319-33858-3_9openalex-snapshot
Scalability in Pattern Mining
inferred← citeshttps://doi.org/10.1007/978-3-319-42996-0_6openalex-snapshot
Multiple Seeds Based Evolutionary Algorithm for Mining Boolean Association Rules
inferred← citeshttps://doi.org/10.1007/978-3-319-94779-2_45openalex-snapshot
Bacterial Colony Algorithms Applied to Association Rule Mining in Static Data and Streams
inferred← citeshttps://doi.org/10.1007/978-3-319-98140-6_1openalex-snapshot
Introduction to Supervised Descriptive Pattern Mining
inferred← citeshttps://doi.org/10.1007/s10115-015-0911-yopenalex-snapshot
Improving a multi-objective evolutionary algorithm to discover quantitative association rules
inferred← citeshttps://doi.org/10.1007/s10288-019-00402-4openalex-snapshot
Metaheuristics for data mining
inferred← citeshttps://doi.org/10.1007/s10479-021-04496-0openalex-snapshot
Metaheuristics for data mining: survey and opportunities for big data
inferred← citeshttps://doi.org/10.1007/s13748-016-0090-4openalex-snapshot
Pattern mining: current status and emerging topics
inferred← citeshttps://doi.org/10.1007/s13748-019-00185-zopenalex-snapshot
A review on the self and dual interactions between machine learning and optimisation
inferred← citeshttps://doi.org/10.1016/j.eswa.2013.12.043openalex-snapshot
Multi-objective PSO algorithm for mining numerical association rules without a priori discretization
inferred← citeshttps://doi.org/10.1016/j.eswa.2017.01.001openalex-snapshot
A new multiple seeds based genetic algorithm for discovering a set of interesting Boolean association rules
inferred← citeshttps://doi.org/10.1016/j.ins.2017.08.043openalex-snapshot
Combining Apriori heuristic and bio-inspired algorithms for solving the frequent itemsets mining problem
inferred← citeshttps://doi.org/10.1016/j.ins.2020.02.073openalex-snapshot
A survey of evolutionary computation for association rule mining
inferred← citeshttps://doi.org/10.1016/j.ipm.2020.102207openalex-snapshot
Heuristics for interesting class association rule mining a colorectal cancer database
inferred← citeshttps://doi.org/10.1016/j.jcss.2013.03.010openalex-snapshot
Discovering gene association networks by multi-objective evolutionary quantitative association rules
inferred← citeshttps://doi.org/10.1016/j.knosys.2021.107419openalex-snapshot
A novel approach based on genetic algorithm to speed up the discovery of classification rules on GPUs
inferred← citeshttps://doi.org/10.1016/j.neucom.2013.01.056openalex-snapshot
Selecting the best measures to discover quantitative association rules
inferred← citeshttps://doi.org/10.1016/j.neucom.2014.10.100openalex-snapshot
Obtaining optimal quality measures for quantitative association rules
inferred← citeshttps://doi.org/10.1016/j.pecs.2021.100904openalex-snapshot
Machine learning technology in biodiesel research: A review
inferred← citeshttps://doi.org/10.1017/s0890060420000396openalex-snapshot
Association rules mining between service demands and remanufacturing services
inferred← citeshttps://doi.org/10.1109/cbd.2014.12openalex-snapshot
An Effective Method for Mining Quantitative Association Rules with Clustering Partition in Satellite Telemetry Data
inferred← citeshttps://doi.org/10.1109/cec.2018.8477978openalex-snapshot
Evolutionary and Immune Algorithms Applied to Association Rule Mining in Static and Stream Data
inferred← citeshttps://doi.org/10.1109/fuzz-ieee.2012.6251173openalex-snapshot
Temporal fuzzy association rule mining with 2-tuple linguistic representation
inferred← citeshttps://doi.org/10.1109/jsyst.2022.3146014openalex-snapshot
A Niching Augmented Evolutionary Algorithm for the Identification of Functional Dependencies in Complex Technical Infrastructures From Alarm Data
inferred← citeshttps://doi.org/10.1109/la-cci.2015.7435950openalex-snapshot
Association rule mining using a bacterial colony algorithm
inferred← citeshttps://doi.org/10.1109/tevc.2013.2290082openalex-snapshot
Survey of Multiobjective Evolutionary Algorithms for Data Mining: Part II
inferred← citeshttps://doi.org/10.1109/tevc.2013.2290086openalex-snapshot
A Survey of Multiobjective Evolutionary Algorithms for Data Mining: Part I
inferred← citeshttps://doi.org/10.1145/2345396.2345566openalex-snapshot
An efficient approach for generating frequent patterns without candidate generation
inferred← citeshttps://doi.org/10.1145/2543581.2543594openalex-snapshot
Survey and analysis of current mobile learning applications and technologies
inferred← citeshttps://doi.org/10.1155/2018/4676258openalex-snapshot
Bacterial Colony Algorithms for Association Rule Mining in Static and Stream Data
inferred← citeshttps://doi.org/10.11606/t.55.2012.tde-16072012-144620openalex-snapshot
Genetic generation of fuzzy knowledge bases: new perspectives
inferred← citeshttps://doi.org/10.2478/amcs-2013-0064openalex-snapshot
Evolutionary algorithms and fuzzy sets for discovering temporal rules
inferred← citeshttps://doi.org/10.2991/ijcis.2018.25905182openalex-snapshot
Optimization of quality measures in association rule mining: an empirical study
inferred← citeshttps://doi.org/10.31577/cai_2023_6_1360openalex-snapshot
Review of Heuristic Algorithms for Frequent Itemsets Mining Problem
inferred← citeshttps://doi.org/10.3233/ica-140479openalex-snapshot
Enhancing the scalability of a genetic algorithm to discover quantitative association rules in large-scale datasets
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