About

The Symposium on Knowledge Discovery, Mining and Learning (KDMiLe) aims at integrating researchers, practitioners, developers, students and users to present theirs research results, to discuss ideas, and to 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 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, 2021, in its ninth edition, KDMiLe will be held completely online from October 4th to October 8th in conjunction with the Brazilian Symposium on Databases (SBBD). This year KDMiLe is being organized by Centro Federal de Educação Tecnológica do Rio de Janeiro (CEFET/RJ).

Important Dates

  • Submission deadline: 26 July 2021 01 August 2021
  • Notification to authors: 02 September 2021
  • Camera ready version: to be defined

Submission 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 EasyChair.

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 2021 registration process can be found here.

Topics of Interest

The KDMiLe Program Committee invites submissions containing new ideas and proposals, and also 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

Keynote speakers

Ana Carolina Lorena (Divisão de Ciência da Computação, ITA)

Ciência de Dados no Enfrentamento de Surtos, Epidemias e Pandemias em Hospitais

Resumo: O Brasil tem enfrentado a proliferação de diversas doenças contagiosas nos últimos anos, sendo o caso mais recente o da pandemia COVID-19. Hospitais de referência em grandes cidades enfrentam muitas dificuldades de gestão de seus recursos e profissionais nesses períodos. E, embora grandes volumes de dados sejam coletados nesses hospitais, sua coleta e organização não é padronizada e há problemas de qualidade dos dados que dificultam realizar análises que dêem suporte a uma tomada de decisão confiável. Nesta palestra são descritos alguns trabalhos em andamento envolvendo diversas etapas da Ciência de Dados com o objetivo de obter modelos preditivos para o suporte à decisão no enfrentamento de surtos, epidemias e pandemias em hospitais. 

Eric Fernandes de Mello Araújo (Departamento de Ciência da Computação, UFLA)

Modelagem Baseada em Agentes Orientada a Dados: Unindo Ciência de Dados e Modelos Teóricos para Compreender a Realidade

Resumo: Avanços nas áreas de aprendizagem de máquina e ciência de dados vêm permitindo o avanço de estudos empíricos em diversas áreas, mas também contribuído para avanços em áreas anteriormente majoritariamente teóricas, ou de difícil análise por meio de dados. Esta palestra irá apresentar os avanços na área de modelagem baseada em agentes com a integração de dados para criação de modelos e simulações que unam conceitos teóricos com informações providas nos mais diversos meios atualmente. Aplicações na área de saúde pública, contágio de comportamento e criminologia nos ajudarão a compreender como as áreas que envolvem a descoberta de conhecimento por meio de dados podem contribuir na compreensão de diversos contextos de grande relevância para a sociedade.

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 – Moacir Antonelli Ponti (ICMC-USP) – moacir@icmc.usp.br
Co-Chair – Diego Furtado Silva (UFSCar) – diegofs@ufscar.br

Local chair

Eduardo Bezerra (CEFET/RJ) – ebezerra@cefet-rj.br

Program committee

Alan ValejoUFSCar
Alexandre PlastinoUFF
André RossiUnesp
Angelo CiarliniLocaliza
Aurora PozoUFPR
Bruno NogueiraUFMS
Carlos SillaPUCPR
Claudia VarassinUFES
Daniela GodoyISISTAN Research Institute
Dayse AlmeidaUFCAT
Denise BandeiraUnisinos
Edson MatsubaraUFMS
Elaine SousaUSP
Erick MazieroUFLA
Fabio CozmanUSP
Filipe VerriUnifesp
Francisco CarvalhoCentro de Informática – CIn/UFPE
Helena CaseliUFSCar
Heloisa CamargoUFSCar
Humberto RazenteUFU
João Fernando MariUFV
João Paulo PapaUnesp
Jonathan SilvaUFMS
Jonice OliveiraUFRJ
Jose A. F. CostaUFRN
Julio NievolaPUCPR
Karin BeckerUFRGS
Leandro MarinhoUFCG
Leonardo RochaUFSJ
Luis ZáratePUCMINAS
Luiz MartinsUFU
Luiz MerschmannUFLA
Marcelino PereiraUERN
Marcelo AlbertiniUFU
Marcilio SoutoUFPE
Marcos GoncalvesUFMG
Marcos QuilesUnifesp
Maria BarioniUFU
Mariá NascimentoUnifesp
Murilo NaldiUFSCar
Rafael GiustiUFAM
Rafael MantovaniUTFPR
Rafael RossiUFMS
Renato TinósUSP
Ricardo CerriUFSCar
Ricardo MarcaciniUSP
Ricardo RiosUFBA
Roberta SinoaraIFSP
Rodrigo BarrosPUCRS
Ronaldo PratiUFABC
Rui CamachoLIACC/FEUP University of Porto
Solange RezendeUSP
Sylvio BarbonUEL
Tatiane NogueiraUFBA
Vinicius SouzaPUCPR
Wagner MeiraUFMG

Sponsors/Promotion

To be defined.


Support Execution Academic Support