Cover Image

Discovering Association Rules in High-Dimensional Small Tabular Data

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Karabulut, Erkan, Daza, Daniel, Groth, Paul, Degeler, Victoria
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Neurosymbolic Association Rule Mining from Tabular Data
by: Karabulut, Erkan, et al.
Published: (2025)

Learning Semantic Association Rules from Internet of Things Data
by: Karabulut, Erkan, et al.
Published: (2024)

Tabular Foundation Models Can Learn Association Rules
by: Karabulut, Erkan, et al.
Published: (2026)

AE SemRL: Learning Semantic Association Rules with Autoencoders
by: Karabulut, Erkan, et al.
Published: (2024)

Semantic Association Rule Learning from Time Series Data and Knowledge Graphs
by: Karabulut, Erkan, et al.
Published: (2023)

DiTEC-project/wdn-knowledge-graph: v1.0.0
by: Erkan Karabulut, et al.
Published: (2025)

Inductive Entity Representations from Text via Link Prediction
by: Daza, Daniel, et al.
Published: (2020)

Too Good To Be True: performance overestimation in (re)current practices for Human Activity Recognition
by: Tello, Andrés, et al.
Published: (2023)

Large-Scale Multipurpose Benchmark Datasets For Assessing Data-Driven Deep Learning Approaches For Water Distribution Networks
by: Tello, Andres, et al.
Published: (2024)

DiTEC-WDN: A Large-Scale Dataset of Hydraulic Scenarios across Multiple Water Distribution Networks
by: Truong, Huy, et al.
Published: (2025)

Benchmarking Synthetic Tabular Data: A Multi-Dimensional Evaluation Framework
by: Sidorenko, Andrey, et al.
Published: (2025)

Towards Synthesizing High-Dimensional Tabular Data with Limited Samples
by: Li, Zuqing, et al.
Published: (2025)

DynaTab: Dynamic Feature Ordering as Neural Rewiring for High-Dimensional Tabular Data
by: Habib, Al Zadid Sultan Bin, et al.
Published: (2026)

Explaining Graph Neural Networks for Node Similarity on Graphs
by: Daza, Daniel, et al.
Published: (2024)

Linear Dimensionality Reduction for Word Embeddings in Tabular Data Classification
by: Ressel, Liam, et al.
Published: (2025)

Learning to Predict, Discover, and Reason in High-Dimensional Event Sequences
by: Math, Hugo
Published: (2026)

Deep Symbolic Learning: Discovering Symbols and Rules from Perceptions
by: Daniele, Alessandro, et al.
Published: (2022)

Highly Imbalanced Regression with Tabular Data in SEP and Other Applications
by: Moukpe, Josias K., et al.
Published: (2025)

Minimizing Hyperbolic Embedding Distortion with LLM-Guided Hierarchy Restructuring
by: Ayoughi, Melika, et al.
Published: (2025)

HyperFast: Instant Classification for Tabular Data
by: Bonet, David, et al.
Published: (2024)

Universal Embeddings of Tabular Data
by: Franz, Astrid, et al.
Published: (2025)

Distributional Regression with Tabular Foundation Models: Evaluating Probabilistic Predictions via Proper Scoring Rules
by: Landsgesell, Jonas, et al.
Published: (2026)

Deep Feature Embedding for Tabular Data
by: Wu, Yuqian, et al.
Published: (2024)

Automated Model Selection for Tabular Data
by: Amballa, Avinash, et al.
Published: (2024)

TabINR: An Implicit Neural Representation Framework for Tabular Data Imputation
by: Ochs, Vincent, et al.
Published: (2025)

Team, Then Trim: An Assembly-Line LLM Framework for High-Quality Tabular Data Generation
by: Zhang, Congjing, et al.
Published: (2026)

Causality for Tabular Data Synthesis: A High-Order Structure Causal Benchmark Framework
by: Tu, Ruibo, et al.
Published: (2024)

CYCLE: Cross-Year Contrastive Learning in Entity-Linking
by: Zhang, Pengyu, et al.
Published: (2024)

LLM Embeddings for Deep Learning on Tabular Data
by: Koloski, Boshko, et al.
Published: (2025)

Tabular Data Generation using Binary Diffusion
by: Kinakh, Vitaliy, et al.
Published: (2024)

Interpretable Graph Neural Networks for Tabular Data
by: Alkhatib, Amr, et al.
Published: (2023)

Fully Test-time Adaptation for Tabular Data
by: Zhou, Zhi, et al.
Published: (2024)

MultiTab: A Comprehensive Benchmark Suite for Multi-Dimensional Evaluation in Tabular Domains
by: Lee, Kyungeun, et al.
Published: (2025)

TIGER: Temporally Improved Graph Entity Linker
by: Zhang, Pengyu, et al.
Published: (2024)

TabDeco: A Comprehensive Contrastive Framework for Decoupled Representations in Tabular Data
by: Chen, Suiyao, et al.
Published: (2024)

Tabular Data Adapters: Improving Outlier Detection for Unlabeled Private Data
by: Herurkar, Dayananda, et al.
Published: (2025)

GEM-T: Generative Tabular Data via Fitting Moments
by: Li, Miao, et al.
Published: (2025)

CLIMB: Class-imbalanced Learning Benchmark on Tabular Data
by: Liu, Zhining, et al.
Published: (2025)

An Explainable Gaussian Process Auto-encoder for Tabular Data
by: Zhang, Wei, et al.
Published: (2025)

Crafting Imperceptible On-Manifold Adversarial Attacks for Tabular Data
by: He, Zhipeng, et al.
Published: (2025)