KDMILE

Symposium on Knowledge Discovery, Mining and Learning (KDMiLe)

October 19-22, 2026

Cuiabá, MT – Brasil

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Topics of Interest

Submission & Presentation Guidelines

ABOUT

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).

TOPICS OF INTEREST

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

SUBMISSION & PRESENTATION GUIDELINES

Papers may be written in Portuguese or English, but the title, the abstract, and the keywords must be written in English.
Submissions are reviewed following a single blind review process, i.e., you do not need to hide authors' names and affiliations.
The manuscript must not exceed eight pages. Papers exceeding this limit will be automatically rejected without being reviewed by the Program Committee.
Papers must be submitted in PDF format. Formats other than PDF will NOT be accepted.
LaTeX template available here http://www.overleaf.com/latex/templates/kdmile-symposium-on-knowledge-discovery-mining-and-learning/qjvchvfvsykz.
Papers must be submitted through JEMS 3 https://jems3.sbc.org.br/kdmile2026.

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.