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Temporal association rule mining: An overview considering the time variable as an integral or implied component
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Outgoing claims: the papers this work cites, builds on, or argues with.
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Calendric Association Rule Mining from Time Series Database
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ASTRA - A Novel interest measure for unearthing latent temporal associations and trends through extending basic gaussian membership function
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Discovery of temporal association rules with hierarchical granular framework
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Mining temporal association rules with frequent itemsets tree
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Improved behavior model based on sequential rule mining
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A hybrid temporal association rules mining method for traffic congestion prediction
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Pediatric population health analysis of southern and central Illinois region: A cross sectional retrospective study using association rule mining and multiple logistic regression
inferredcites →https://doi.org/10.1016/j.cor.2013.09.018openalex-snapshot
Optimizing frequent time-window selection for association rules mining in a temporal database using a variable neighbourhood search
inferredcites →https://doi.org/10.1016/j.csda.2008.01.028openalex-snapshot
Detection of unknown computer worms based on behavioral classification of the host
inferredcites →https://doi.org/10.1016/j.datak.2006.10.009openalex-snapshot
ARMADA – An algorithm for discovering richer relative temporal association rules from interval-based data
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Mining fuzzy periodic association rules
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An efficient algorithm for incremental mining of temporal association rules
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A knowledge-driven method of adaptively optimizing process parameters for energy efficient turning
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Eliciting and utilising knowledge for security event log analysis: An association rule mining and automated planning approach
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Designing and implementing a collaborative health knowledge system
inferredcites →https://doi.org/10.1016/j.future.2017.01.013openalex-snapshot
G-SPAMINE: An approach to discover temporal association patterns and trends in internet of things
inferredcites →https://doi.org/10.1016/j.ins.2004.07.006openalex-snapshot
From intra-transaction to generalized inter-transaction: Landscaping multidimensional contexts in association rule mining
inferredcites →https://doi.org/10.1016/j.ins.2018.02.027openalex-snapshot
A novel approach for software defect prediction through hybridizing gradual relational association rules with artificial neural networks
inferredcites →https://doi.org/10.1016/j.is.2009.01.002openalex-snapshot
Non-redundant sequential rules—Theory and algorithm
inferredcites →https://doi.org/10.1016/j.jbi.2018.03.002openalex-snapshot
Incorporating repeating temporal association rules in Naïve Bayes classifiers for coronary heart disease diagnosis
inferredcites →https://doi.org/10.1016/j.joi.2010.10.002openalex-snapshot
An approach for detecting, quantifying, and visualizing the evolution of a research field: A practical application to the Fuzzy Sets Theory field
inferredcites →https://doi.org/10.1016/j.jss.2008.07.037openalex-snapshot
Mining temporal interval relational rules from temporal data
inferredcites →https://doi.org/10.1016/j.knosys.2011.07.005openalex-snapshot
CMRules: Mining sequential rules common to several sequences
inferredcites →https://doi.org/10.1016/j.knosys.2017.02.001openalex-snapshot
Extraction and optimization of classification rules for temporal sequences: Application to hospital data
inferredcites →https://doi.org/10.1016/j.knosys.2018.07.031openalex-snapshot
LTARM: A novel temporal association rule mining method to understand toxicities in a routine cancer treatment
inferredcites →https://doi.org/10.1016/j.patcog.2016.07.024openalex-snapshot
Mining intricate temporal rules for recognizing complex activities of daily living under uncertainty
inferredcites →https://doi.org/10.1016/j.procs.2017.08.069openalex-snapshot
TWINCLE : A Constrained Sequential Rule Mining Algorithm for Event Logs
inferredcites →https://doi.org/10.1016/j.rse.2019.04.025openalex-snapshot
An efficient approach to capture continuous impervious surface dynamics using spatial-temporal rules and dense Landsat time series stacks
inferredcites →https://doi.org/10.1016/j.scico.2010.10.003openalex-snapshot
Mining quantified temporal rules: Formalism, algorithms, and evaluation
inferredcites →https://doi.org/10.1016/j.tele.2015.10.005openalex-snapshot
Change discovery of learning performance in dynamic educational environments
inferredcites →https://doi.org/10.1016/s0169-023x(01)00003-9openalex-snapshot
Inter-transactional association rules for multi-dimensional contexts for prediction and their application to studying meteorological data
inferredcites →https://doi.org/10.1016/s0169-023x(02)00135-0openalex-snapshot
Discovering calendar-based temporal association rules
inferredcites →https://doi.org/10.1016/s0957-4174(01)00037-9openalex-snapshot
Mining the change of customer behavior in an internet shopping mall
inferredcites →https://doi.org/10.1023/a:1009748302351openalex-snapshot
Discovery of Frequent Episodes in Event Sequences
inferredcites →https://doi.org/10.1023/a:1025824629047openalex-snapshot
Sequential Association Rule Mining with Time Lags
inferredcites →https://doi.org/10.1023/b:scie.0000037368.31217.34openalex-snapshot
A new technique for building maps of large scientific domains based on the cocitation of classes and categories
inferredcites →https://doi.org/10.1080/14740338.2017.1325463openalex-snapshot
Methods for safety signal detection in healthcare databases: a literature review
inferredcites →https://doi.org/10.1093/jamia/ocw048openalex-snapshot
Learning temporal rules to forecast instability in continuously monitored patients
inferredcites →https://doi.org/10.1109/access.2019.2962619openalex-snapshot
A Spatiotemporal Apriori Approach to Capture Dynamic Associations of Regional Traffic Congestion
inferredcites →https://doi.org/10.1109/csss.2012.500openalex-snapshot
Association Rules Mining for Academic Cooperation Based on Time Extension and Duration Accumulation
inferredcites →https://doi.org/10.1109/fskd.2010.5569793openalex-snapshot
Mining generalized fuzzy association rules from Web logs
inferredcites →https://doi.org/10.1109/fuzz.2002.1006622openalex-snapshot
Fuzzy data mining for discovering changes in association rules over time
inferredcites →https://doi.org/10.1109/fuzzy.2005.1452495openalex-snapshot
A Novel Approach to Mining Inter-Transaction Fuzzy Association Rules from Stock Price Variation Data
inferredcites →https://doi.org/10.1109/icde.1998.655777openalex-snapshot
Point-versus interval-based temporal data models
inferredcites →https://doi.org/10.1109/icde.1999.754913openalex-snapshot
Efficient mining of partial periodic patterns in time series database
inferredcites →https://doi.org/10.1109/icsmc.2007.4413739openalex-snapshot
Data mining and fuzzy inference based salinity and temperature variation prediction
inferredcites →https://doi.org/10.1109/nbis.2012.93openalex-snapshot
An Approach Mining Cyclic Association Rules in E-Commerce
inferredcites →https://doi.org/10.1109/risp.1990.63857openalex-snapshot
Adaptive real-time anomaly detection using inductively generated sequential patterns
inferredcites →https://doi.org/10.1109/tkde.2002.1019212openalex-snapshot
A survey of temporal knowledge discovery paradigms and methods
inferredcites →https://doi.org/10.1109/tkde.2003.1161581openalex-snapshot
Efficient mining of intertransaction association rules
inferredcites →https://doi.org/10.1109/tkde.2003.1209015openalex-snapshot
Progressive partition miner: An efficient algorithm for mining general temporal association rules
inferredcites →https://doi.org/10.1109/tkde.2005.12openalex-snapshot
Linear temporal sequences and their interpretation using midpoint relationships
inferredcites →https://doi.org/10.1109/tkde.2005.181openalex-snapshot
Discovering frequent episodes and learning hidden Markov models: a formal connection
inferredcites →https://doi.org/10.1109/tkde.2007.1055openalex-snapshot
Discovering Frequent Generalized Episodes When Events Persist for Different Durations
inferredcites →https://doi.org/10.1109/tkde.2015.2405509openalex-snapshot
Mining Partially-Ordered Sequential Rules Common to Multiple Sequences
inferredcites →https://doi.org/10.1109/tkde.2017.2773493openalex-snapshot
Mining Precise-Positioning Episode Rules from Event Sequences
inferredcites →https://doi.org/10.1109/tsmcb.2004.835352openalex-snapshot
Discovery of Fuzzy Temporal Association Rules
inferredcites →https://doi.org/10.1109/tsp.2018.2883014openalex-snapshot
Quickest Search and Learning Over Correlated Sequences: Theory and Application
inferredcites →https://doi.org/10.1145/1084805.1084818openalex-snapshot
Efficient calendar based temporal association rule
inferredcites →https://doi.org/10.1145/1132960.1132963openalex-snapshot
Interestingness measures for data mining
inferredcites →https://doi.org/10.1145/1656274.1656280openalex-snapshot
KNIME - the Konstanz information miner
inferredcites →https://doi.org/10.1145/170035.170072openalex-snapshot
Mining association rules between sets of items in large databases
inferredcites →https://doi.org/10.1145/182.358434openalex-snapshot
Maintaining knowledge about temporal intervals
inferredcites →https://doi.org/10.1145/2431211.2431218openalex-snapshot
Sequential pattern mining -- approaches and algorithms
inferredcites →https://doi.org/10.1145/2832987.2833069openalex-snapshot
An Approach for Mining Similarity Profiled Temporal Association Patterns Using Gaussian Based Dissimilarity Measure
inferredcites →https://doi.org/10.1145/335603.335770openalex-snapshot
An approach to discovering temporal association rules
inferredcites →https://doi.org/10.1145/358108.358114openalex-snapshot
Beyond intratransaction association analysis
inferredcites →https://doi.org/10.1145/775047.775053openalex-snapshot
Selecting the right interestingness measure for association patterns
inferredcites →https://doi.org/10.1145/775047.775109openalex-snapshot
Sequential PAttern mining using a bitmap representation
inferredcites →https://doi.org/10.1177/0047287517692446openalex-snapshot
Travel Diaries Analysis by Sequential Rule Mining
inferredcites →https://doi.org/10.1186/1471-2105-10-s3-s6openalex-snapshot
Identification of temporal association rules from time-series microarray data sets
inferredcites →https://doi.org/10.1186/s12911-019-0810-3openalex-snapshot
GatewayNet: a form of sequential rule mining
inferredcites →https://doi.org/10.15598/aeee.v17i1.3185openalex-snapshot
Discovering Periodic Itemsets Using Novel Periodicity Measures
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Evolutionary algorithms and fuzzy sets for discovering temporal rules
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A functional temporal association mining approach for screening potential drug–drug interactions from electronic patient databases
inferredcites →https://doi.org/10.3233/ida-140668openalex-snapshot
Understanding episode mining techniques: Benchmarking on diverse, realistic, artificial data
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Measuring the accuracy and interest of association rules: A new framework
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Sequential pattern mining algorithms review
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From concrete to inferred knowledge: Enhanced mining constraint-based cyclic association rules from medical social network
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Mining Health Care Administrative Data with Temporal Association Rules on Hybrid Events
inferredcites →https://doi.org/10.5120/7889-1253openalex-snapshot
Cyclic Association Rules Mining under Constraints
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Data mining: concepts and techniques
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Mining Non-Redundant Inter-Transaction Rules
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Efficient Mining of Profit Rules from Closed Inter-Transaction Itemsets
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Hybridization of Apriori Algorithm and Genetic Algorithm for Association Rule Mining in Generative AI Enabled Machine Learning
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Multi-domain Anomalous Relationships in Heterogeneous Temporal Data
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ARTmine: Automatic Association Rule Mining with Temporal Behavior for Hardware Verification
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Discovering temporal frequent patterns: An optimized approach
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“Book Music” Representation for Temporal Data, as a Part of the Feature Extraction Process: A Novel Approach to Improve the Handling of Time-Dependent Data in Secondary Use of Healthcare Structured Data
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Extracting Temporal Relationships in EHR: Application to COVID-19 Patients
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Temporal Characterization and Visualization of Revolving Therapy-Events in Lung Cancer Patients
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Chimp Optimization Algorithm Based Feature Selection with Machine Learning for Medical Data Classification
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Temporal Association Rule Mining: Race-Based Patterns of Treatment-Adverse Events in Breast Cancer Patients Using SEER–Medicare Dataset
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