Symposium on Knowledge Discovery, Mining and Learning (KDMiLe)
The Symposium on Knowledge Discovery, Mining and Learning (KDMiLe) aims at integrating researchers, practitioners, developers, students, and users to present their research results, discuss ideas, and exchange techniques, tools, and practical experiences – related to the Data Mining and Machine Learning areas.
KDMiLe originated from WAAMD (Workshop em Algoritmos e Aplicações de Mineração de Dados) that occurred during five years – 2005 to 2009 – as a Workshop of the Brazilian Symposium on Databases (SBBD). Since 2013, KDMiLe has been organized alternatively in conjunction with the Brazilian Conference on Intelligent Systems (BRACIS) and the Brazilian Symposium on Databases (SBBD).
This year, 2026, in its fourteenth edition, KDMiLe will be held in Cuiabá, Mato Grosso, from October 19 to October 22, in conjunction with the Brazilian Conference on Intelligent Systems (BRACIS), and organized by Federal University of Mato Grosso (UFMT).
The KDMiLe Program Committee invites submissions containing new ideas, proposals, and applications in the Data Mining and Machine Learning areas. Below is a list of common topics, although KDMiLe is not limited to them.
Association Rules
Classification
Clustering
Data Mining Applications
Data Mining Foundations
Evaluation Methodology in Data Mining
Feature Selection and Dimensionality Reduction
Graph Mining
Massive Data Mining
Multimedia Data Mining
Multirelational Mining
Outlier Detection
Parallel and Distributed Data Mining
Pre and Post Processing
Ranking and Preference Mining
Privacy and Security in Data Mining
Quality and Interest Metrics
Sequential Patterns
Social Network Mining
Stream Data Mining
Text Mining
Time-Series Analysis
Visual Data Mining
Web Mining
Recommender Systems based on Data Mining
Active Learning
Bayesian Inference
Case-Based Reasoning
Cognitive Models of Learning
Constructive Induction and Theory Revision
Cost-Sensitive Learning
Data-Centric Artificial Intelligence
Deep Learning
Ensemble Methods
Evaluation Methodology in Machine Learning
Fuzzy Learning Systems
Inductive Logic
Kernel Methods
Knowledge-Intensive Learning
Learning Theory
Machine Learning Applications
Meta-Learning
Multi-Agent and Co-Operative Learning
Natural Language Processing
Online Learning
Probabilistic and Statistical Methods
Ranking and Preference Learning
Recommender Systems based on Machine
Reinforcement Learning
Semi-Supervised Learning
Supervised Learning
Unsupervised Learning
Papers submitted to KDMiLe must not have been simultaneously submitted to any other forum (conference or journal), nor should they have already been published elsewhere. The acceptance of a paper implies that at least one of its authors will register for the symposium to present it.
Submitted papers will be reviewed based on originality, relevance, technical soundness, and clarity of presentation. Accepted papers will be published electronically in the KDMiLe proceedings.
In all past editions, authors of selected papers accepted for presentation in KDMiLe have been invited to submit extended and revised versions of these papers to a special issue of JIDM (Journal of Information and Database Management). This year, we intend to follow this same policy of encouraging the best submissions with publication in an international journal.
Submission deadline
June 8th, 2026
Notification to authors
July 20th, 2026
Camera-ready version
August 28th, 2026
Note
All deadlines are 23:59 UTC-12:00 – anywhere on Earth!
JS
Program Chairs
Cefet-RJ
TBA
Co-Chair
TBA
AP
Steering Committee
UFF
AC
Steering Committee
ICMC-USP
LM
Steering Committee
UFLA
RC
Steering Committee
ICMC-USP
WM
Steering Committee
UFMG