Association Rule Mining

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8 papers1 paper in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Mining Frames: Association Rule Mining as an Unsupervised Framework for News Framing Detection
Oct 22, 2026Hager Hesham MustafaAssociation Rule Mining
Anmat Computational Analysis Lab
Human Activity Recognition via Ultra-Wideband Data: A Framework for Dimensionality Reduction, Pattern Discovery, and Predictive Modeling
Sep 25, 2026Nahid Sahel Gozin, Reza Sedaghat, Prathap SiddavaatamAssociation Rule Mining Human Activity Recognition
OPR-AL Lab, Toronto Metropolitan University, 350 Victoria Street, Toronto, ON M5B 2K3, Canada
Interpretable Crisis Behavior Analysis Using Mobility and Social Media Data
Jun 8, 2026Muhammad Hamza Arshad Majeed, Sidahmed Benabderrahmane, Talal RahwanSocial Media Analysis Association Rule Mining
New York University (NYUAD), Division of Science, Computer Science Department
A Context-Aware Middleware for Medical Image Based Reports: An approach based on image feature extraction and association rules
May 29, 2026Erick O. Rodrigues, Jose Viterbo, Aura Conci +1Medical Imaging Association Rule Mining
Department of Computer Science Universidade Federal Fluminense Niter6i, Rio de Janeiro · Departament of Mathematics & Statistics, York University Keele St 4700, Toronto, ON M3J IP3, Canada
Auditing Discriminatory Patterns in Mortgage Lending Through Association Rules and Fair Binning
May 16, 2026Archit Rathod, Dhwani Chande, Het NagdaFinancial Services Association Rule Mining
University of Illinois Chicago
Assessing the Role of Intersection Proximity in Pedestrian Crashes: Insights from Data Mining Approach
Apr 30, 2026Ahmed Hossain, Xiaoduan Sun, Subasish DasAssociation Rule Mining
Traffic Safety Analysis Engineer, Multimodal Planning Division (MPD) Arizona Department of Transportation (ADOT) 206 S 17th Ave, Phoenix, Arizona, 85007 · Department of Civil Engineering University of Louisiana at Lafayette, Lafayette, Louisiana, 70503 · College of Science of Engineering, Texas State University 601 University Drive, San Marcos, Texas 78666-4684
Apriori-based Analysis of Learned Helplessness in Mathematics Tutoring: Behavioral Patterns by Level, Intervention, and Outcome
Apr 29, 2026John Paul P. MirandaAssociation Rule Mining Intelligent Tutoring Systems
Pampanga State University, Philippines
Tabular Foundation Models Can Learn Association Rules
Feb 16, 2026Erkan Karabulut, Daniel Daza, Paul Groth +2Association Rule Mining Tabular Foundation Models
University of Amsterdam, The Netherlands · Vrije Universiteit Amsterdam, The Netherlands · Amsterdam UMC, location VUmc, Vrije Universiteit Amsterdam, the Netherlands

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Mining Frames: Association Rule Mining as an Unsupervised Framework for News Framing Detection

Hager Hesham Mustafa

Anmat Computational Analysis Lab

Association Rule Mining

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Computational framing analysis is dominated by supervised classifiers that require large annotated corpora and predefined frame taxonomies, limiting their use for non-English languages, novel events, and low-resource settings. This paper presents Association Rule Mining (ARM) as an unsupervised, annotation-free framework for news framing detection. Each document or sentence is treated as a transaction of lexical items, and high-lift co-occurrence rules are read as candidate framing signals (Gamson and Modigliani's condensing symbols), confirmed in context by journalist-researchers. The primary validation is Framing Gaza: 25,512 English-language articles from 16 US, UK, French, and German outlets covering the first year of the Gaza war (October 2023 to October 2024), analyzed with Apriori (mlxtend), platform-weighted support, POS-filtered n-grams, and lift-based scoring. ARM inductively surfaces interpretable patterns without predefined categories: a cross-platform military frame, {israeli, strike} to {kill}, appears as a closed rule in 14 of 16 outlets, and a month-by-month analysis recovers event-driven framing pivots (e.g., the April 2024 World Central Kitchen strike) with no prior event labeling. A systematic headline-body divergence is identified, with headlines carrying concentrated, high-lift editorial framing and body text reproducing institutional, procedural language. A methodological validation on the Seven Days project (1,575 Arabic articles on the 2026 Iran war) shows that FP-Growth enables sentence-level analysis with far lower memory overhead. Findings were validated through editorial uptake: two published investigative stories (Daraj Media; Saheeh Masr). Preprocessing code is released openly; the full pipeline is documented but withheld to preserve operational deployability.