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A Learning Theory Framework for Association Rules and Sequential Events

https://doi.org/10.2139/ssrn.1868446
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Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-1-4615-1627-9_2openalex-extracted
Personalization of Supermarket Product Recommendations
inferredcites →https://doi.org/10.1007/978-3-540-44918-8_1openalex-extracted
Choosing the Right Lens: Finding What is Interesting in Data Mining
inferredcites →https://doi.org/10.1007/978-3-540-48247-5_50openalex-extracted
Efficient Mining of High Confidence Association Rules without Support Thresholds
inferredcites →https://doi.org/10.1007/978-3-540-68125-0_92openalex-extracted
Mining Non-coincidental Rules without a User Defined Support Threshold
inferredcites →https://doi.org/10.1016/j.patrec.2009.05.002openalex-extracted
A lower bound on the sample size needed to perform a significant frequent pattern mining task
inferredcites →https://doi.org/10.1016/s0169-023x(04)00110-7openalex-extracted
Microarray gene expression data association rules mining based on BSC-tree and FIS-tree
inferredcites →https://doi.org/10.1023/a:1013284820704openalex-extracted
Efficient Adaptive-Support Association Rule Mining for Recommender Systems
inferredcites →https://doi.org/10.1023/b:mach.0000035473.11134.83openalex-extracted
Lessons and Challenges from Mining Retail E-Commerce Data
inferredcites →https://doi.org/10.1109/72.788640openalex-extracted
An overview of statistical learning theory
inferredcites →https://doi.org/10.1109/icde.2000.839448openalex-extracted
Finding interesting associations without support pruning
inferredcites →https://doi.org/10.1109/icdm.2002.1184048openalex-extracted
Discovery of interesting association rules from Livelink web log data
inferredcites →https://doi.org/10.1109/tit.1979.1056087openalex-extracted
Distribution-free performance bounds for potential function rules
inferredcites →https://doi.org/10.1145/1390334.1390381openalex-extracted
Learning to rank at query-time using association rules
inferredcites →https://doi.org/10.1145/1553374.1553449openalex-extracted
Generalization analysis of listwise learning-to-rank algorithms
inferredcites →https://doi.org/10.1145/170035.170072openalex-extracted
Mining association rules between sets of items in large databases
inferredcites →https://doi.org/10.1145/502512.502526openalex-extracted
Empirical bayes screening for multi-item associations
inferredcites →https://doi.org/10.1145/502598.502601openalex-extracted
Mining confident rules without support requirement
inferredcites →https://doi.org/10.1145/775047.775053openalex-extracted
Selecting the right interestingness measure for association patterns
inferredcites →https://doi.org/10.1145/775047.775109openalex-extracted
Sequential PAttern mining using a bitmap representation
inferredcites →https://doi.org/10.1145/775052.775053openalex-extracted
Selecting the right interestingness measure for association patterns
inferredcites →https://doi.org/10.1214/aos/1176344196openalex-extracted
A Finite Sample Distribution-Free Performance Bound for Local Discrimination Rules
inferredcites →https://doi.org/10.1214/ss/1042727943openalex-extracted
The Mixture Transition Distribution Model for High-Order Markov Chains and Non-Gaussian Time Series
inferredcites →https://doi.org/10.2139/ssrn.1736062openalex-extracted
A Hierarchical Model for Association Rule Mining of Sequential Events: An Approach to Automated Medical Symptom Prediction
inferredcites →https://doi.org/10.48550/arxiv.1301.7363openalex-extracted
Empirical Analysis of Predictive Algorithms for Collaborative Filtering

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inferred← citeshttps://doi.org/10.1214/11-aoas522openalex-snapshot
Bayesian hierarchical rule modeling for predicting medical conditions
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