Symposium on Knowledge Discovery, Mining and Learning

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, 2023, in its eleventh edition, KDMiLe will be held in Belo Horizonte, Minas Gerais, from 25-29 September in conjunction with the Brazilian Symposium on Databases (SBBD). This year KDMiLe is being organized by PUC Minas and Instituto Federal de Minas Gerais.

Important Dates

  • Submission deadline: May 13th, 2023
  • Notification to authors: July 24th, 2023
  • Camera-ready version: July 31st, 2023

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 8 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.
  • Papers must be submitted through JEMS.

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. A preliminary version of the proceedings, including all the accepted papers, will be available to the symposium attendees.

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.

Registration

In this edition, registration to KDMiLe will happen along with SBBD. Further details on the SBBD/KDMiLe 2023 registration process can be found here.

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.

In Data Mining

  • 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

In Machine Learning

  • Active Learning
  • Bayesian Inference
  • Case-Based Reasoning
  • Cognitive Models of Learning
  • Constructive Induction and Theory Revision
  • Cost-Sensitive Learning
  • 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
  • Probabilistic and Statistical Methods
  • Ranking and Preference Learning
  • Recommender Systems based on Machine Learning
  • Reinforcement Learning
  • Semi-Supervised Learning
  • Supervised Learning
  • Unsupervised Learning
  • Online Learning

Committees

Steering committee

Luiz Henrique de Campos Merschmann (UFLA)
Alexandre Plastino (UFF)
André Carlos Ponce de Leon Ferreira de Carvalho (ICMC-USP)
Wagner Meira Jr. (UFMG)
Ricardo Cerri (UFSCAR)

Program chair

Chair – Aline Paes (UFF) – alinepaes@ic.uff.br
Co-Chair – Eduardo Bezerra (CEFET-RJ) – ebezerra@cefet-rj.br

Local chair

Renato Vimieiro (UFMG) – rvimieiro@dcc.ufmg.br

Program Committee

TBA