Data Mining Algorithms and Applications

Latest papers

• Optimal or Greedy Decision Trees? Revisiting their Objectives, Tuning, and Performance

  Jacobus G. M. van der Linden, Dani\"el Vos, Mathijs M. de Weerdt, Sicco Verwer, Emir Demirovi\'c · 7 August 2026

  Recently there has been a surge of interest in optimal decision tree (ODT) methods that globally optimize accuracy directly, in contrast to traditional approaches that locally optimize an impurity or information metric. However, the literature shows conflicting evidence on the value of ODTs, with so…

• Fast Discovery of Inclusion Dependencies with Desbordante

  Alexander Smirnov, Anton Chizhov, Ilya Shchuckin, Nikita Bobrov, George Chernishev · 4 August 2026

  Inclusion dependency is a relation between attributes of tables that indicates possible Primary Key-Foreign Key references. Automatic discovery of inclusion dependencies is a relevant problem for both academic and industrial communities. The core concern for this problem is the efficiency of discove…

• Understanding Context Sampling in TabPFN on Small Tabular Datasets

  Mohammed Abdullah · 30 July 2026

  TabPFN performs classification through in-context learning: it conditions on a set of labeled training rows (the context, or prototypes) and predicts test labels without gradient updates. On small tabular datasets, practitioners must still choose the context size and which rows constitute the contex…

• Discovering Frequent Closed Embedded Sub-DAGs in Spatio-Temporal Event Data

  Piotr S. Maci\k{a}g · 8 July 2026

  We propose a novel approach to mine patterns in spatio-temporal event data based on discovering frequent closed embedded sub-Directed Acyclic Graphs (DAGs). In our method, event instances are represented as nodes labelled by event types, while edges capture spatio-temporal following relationships. W…

• Scalable Maximal Frequent Episode Mining with Desbordante

  Maxim Ivanov, Matvei Smirnov, Alisa Strazdina, George Chernishev · 7 July 2026

  Episode mining aims to extract subsequences of events that possess certain distinctive properties and constitute facts valuable to the user. Maximal frequent episode mining concentrates on discovery of frequently-appearing subsequences, which are not included into any other larger frequent subsequen…

• Clustering with Non-adaptive Subset Queries

  Hadley Black, Euiwoong Lee, Arya Mazumdar, Barna Saha · 30 June 2026

  Recovering the underlying $k$-clustering of a set $U$ of $n$ points by asking pair-wise same-cluster queries has garnered significant interest in the past few years. Given a query $S \subset U$, $|S|=2$, the oracle returns "yes" if the points are in the same cluster and "no" otherwise. For adaptive …

• AlphaMemo: Structured Search-Process Memory for Self-Evolving Alpha Mining Agents

  Hang Yu, Zifan Zheng, Jeff Z. Pan, Tongliang Liu, Zhiyong Wang, Fengxiang He · 23 June 2026

  LLM agents are promising for alpha mining via combining financial priors, symbolic reasoning, executable factor generation, and feedback-driven refinement. Yet, they face a combinatorial search space, noisy non-stationary feedback, redundant discoveries, and overfitting risks from naively reusing pa…

• Few-Shot Resampling for Scalable Statistically-Sound Data Mining

  Leonardo Pellegrina, Fabio Vandin · 11 June 2026

  A key step in knowledge discovery is the evaluation of data mining results. In several applications, including pattern mining, graph analysis, and others, this step includes the evaluation of the statistical significance of the results, to avoid spurious discoveries due only to noise or random fluct…

• Discovering Data Structures: Nearest Neighbor Search and Beyond

  Omar Salemohamed, Laurent Charlin, Shivam Garg, Vatsal Sharan, Gregory Valiant · 9 June 2026

  We propose a general framework for end-to-end learning of data structures. Our framework adapts to the underlying data distribution and provides fine-grained control over query and space complexity. Crucially, the data structure is learned from scratch, and does not require careful initialization or…

• Frequency-based Constrained Sampling for Interval Patterns

  Djawad Bekkoucha, Abdelkader Ouali, Bruno Cr\'emilleux · 9 June 2026

  Output space pattern sampling is a powerful alternative to exhaustive pattern mining for exploring large pattern spaces, as it enables users to focus on representative patterns drawn according to a chosen interestingness measure. In this paper, we address the problem of sampling interval patterns un…

• Small Language Model Agents Enable Efficient and High-Quality Knowledge Mining

  Sipeng Zhang, Shuhuai Lin, Xinpeng Wei, Yihang Chen, Pin Qian, Su Wang, Huan Xu · 8 June 2026

  At the core of Deep Research is knowledge mining, the task of extracting structured information from massive unstructured text in response to user instructions. Large language models (LLMs) excel at interpreting such instructions but are prohibitively expensive to deploy at scale, while traditional …

• Optimal Pattern Detection Tree for Symbolic Rule-Based Classification

  Young-Chae Hong, Yangho Chen · 15 May 2026

  Pattern discovery in data plays a crucial role across diverse domains, including healthcare, risk assessment, and machinery maintenance. In contrast to black-box deep learning models, symbolic rule discovery emerges as a key data mining task, generating human-interpretable rules that offer both tran…

• Decision Tree Learning on Product Spaces

  Arshia Soltani Moakahr, Faraz Ghahremani, Kiarash Banihashem, MohammadTaghi Hajiaghayi · 14 May 2026

  Decision tree learning has long been a central topic in theoretical computer science, driven by its practical importance. A fundamental and widely used method for decision tree construction is the top-down greedy heuristic, which recursively splits on the most influential variable. Despite its empir…

• Out of Spuriousity: Improving Robustness to Spurious Correlations without Group Annotations

  Phuong Quynh Le, J\"org Schl\"otterer, Christin Seifert · 28 April 2026

  Machine learning models are known to learn spurious correlations, i.e., features having strong relations with class labels but no causal relation. Relying on those correlations leads to poor performance in the data groups without these correlations and poor generalization ability. To improve the rob…

• Variable Selection Using Relative Importance Rankings

  Tien-En Chang, Argon Chen · 14 April 2026

  Although conceptually related, variable selection and relative importance (RI) analysis have been treated quite differently in the literature. While RI is typically used for post-hoc model explanation, this paper explores its potential for variable or feature ranking and filter-based selection befor…

• Relational Transformer: Toward Zero-Shot Foundation Models for Relational Data

  Rishabh Ranjan, Valter Hudovernik, Mark Znidar, Charilaos Kanatsoulis, Roshan Upendra, Mahmoud Mohammadi, Joe Meyer, Tom Palczewski, Carlos Guestrin, Jure Leskovec · 3 March 2026

  Pretrained transformers readily adapt to new sequence modeling tasks via zero-shot prompting, but relational domains still lack architectures that transfer across datasets and tasks. The core challenge is the diversity of relational data, with varying heterogeneous schemas, graph structures and func…

• Beyond Mimicry to Contextual Guidance: Knowledge Distillation for Interactive AI

  Tong Wang, K. Sudhir · 23 February 2026

  As large language models increasingly mediate firm - customer interactions, firms face a tradeoff: the most capable models perform well but are costly and difficult to control at scale. Existing knowledge distillation methods address this challenge by training weaker, deployable models to imitate fr…

• Tabular Foundation Models Can Learn Association Rules

  Erkan Karabulut, Daniel Daza, Paul Groth, Martijn C. Schut, Victoria Degeler · 17 February 2026

  Association Rule Mining (ARM) is a fundamental task for knowledge discovery in tabular data and is widely used in high-stakes decision-making. Classical ARM methods rely on frequent itemset mining, leading to rule explosion and poor scalability, while recent neural approaches mitigate these issues b…

• AI Driven Discovery of Bio Ecological Mediation in Cascading Heatwave Risks

  Yiquan Wang, Tin-Yeh Huang, Qingyun Gao, Yuhan Chang, Jialin Zhang · 12 February 2026

  Compound heatwaves increasingly trigger complex cascading failures that propagate through interconnected physical and human systems, yet the fragmentation of disciplinary knowledge hinders the comprehensive mapping of these systemic risk topologies. This study introduces the Heatwave Discovery Agent…

• Predictive Query Language: A Domain-Specific Language for Predictive Modeling on Relational Databases

  Vid Kocijan, Jinu Sunil, Jan Eric Lenssen, Viman Deb, Xinwei Xe, Federco Reyes Gomez, Matthias Fey, Jure Leskovec · 11 February 2026

  The purpose of predictive modeling on relational data is to predict future or missing values in a relational database, for example, future purchases of a user, risk of readmission of the patient, or the likelihood that a financial transaction is fraudulent. Typically powered by machine learning meth…

• Mining Generalizable Activation Functions

  Alex Vitvitskyi, Michael Boratko, Matej Grcic, Razvan Pascanu, Deep Shah, Petar Veli\v{c}kovi\'c · 6 February 2026

  The choice of activation function is an active area of research, with different proposals aimed at improving optimization, while maintaining expressivity. Additionally, the activation function can significantly alter the implicit inductive bias of the architecture, controlling its non-linear behavio…

• LLM-Based Multi-Agent Blackboard System for Information Discovery in Data Science

  Alireza Salemi, Mihir Parmar, Palash Goyal, Yiwen Song, Jinsung Yoon, Hamed Zamani, Tomas Pfister, Hamid Palangi · 3 February 2026

  Advances in large language models (LLMs) have created new opportunities in data science, but their deployment is often limited by the challenge of finding relevant data in large data lakes. Existing methods struggle with this: both single- and multi-agent systems are quickly overwhelmed by large, he…

• Unlocking Graph Structure Learning with Tree-Guided Large Language Models

  Zhihan Zhang, Xunkai Li, Lei Zhu, Guang Zeng, Bowen Fan, Yanzhe Wen, Hongchao Qin, Rong-Hua Li, Guoren Wang · 3 February 2026

  Recently, the emergence of large language models (LLMs) has motivated integrating language descriptions into graphs, forming text-attributed graphs (TAGs) that enhance model encoding capabilities from a data-centric perspective. A review of prior advancements highlights that graph structure learning…

• When LLM Agents Meet Graph Optimization: An Automated Data Quality Improvement Approach

  Zhihan Zhang, Xunkai Li, Yilong Zuo, Yanzhe Wen, Zhaoxin Fan, Zhenjun Li, Bing Zhou, Rong-Hua Li, Guoren Wang · 3 February 2026

  Text-attributed graphs (TAGs) have become a key form of graph-structured data in modern data management and analytics, combining structural relationships with rich textual semantics for diverse applications. However, the effectiveness of analytical models, particularly graph neural networks (GNNs), …

• EMP: Enhance Memory in Data Pruning

  Jinying Xiao, Ping Li, Jie Nie, Bin Ji, Shasha Li, Xiaodong Liu, Jun Ma, Qingbo Wu, Jie Yu · 13 January 2026

  Recently, large language and vision models have shown strong performance, but due to high pre-training and fine-tuning costs, research has shifted towards faster training via dataset pruning. Previous methods used sample loss as an evaluation criterion, aiming to select the most "difficult" samples …