The Symposium on Knowledge Discovery, Mining and Learning (KDMiLe) aims at integrating researchers, practitioners, developers, students and users to present their research results, to discuss ideas, and to exchange techniques, tools, and practical experiences related to the Data Mining and Machine Learning areas. KDMiLe is organized alternately in conjunction with the Brazilian Conference on Intelligent Systems (BRACIS) and the Brazilian Symposium on Databases (SBBD). This year, 2019, in its seventh edition, KDMiLe will be held in Fortaleza, Ceará, on October 07th to 10th in conjunction with the Brazilian Symposium on Databases (SBBD). The KDMiLe Program Committee invites submissions containing new ideas and proposals, and also applications, in the Data Mining and Machine Learning areas. Submitted papers will be reviewed based on originality, relevance, technical soundness and clarity of presentation.

This year KDMiLe is being organized by Federal University of Ceará (UFC) and Centro Universitário 7 de Setembro (UNI7). As in the last year, KDMiLe comes with two different tracks: Algorithms and Applications.

  • Applications Track: authors are encouraged to submit papers reporting applications of Machine Learning and Data Mining methods in different areas.
  • Algorithms Track: authors are encouraged to submit papers describing new ideas and concepts in Machine Learning and Data Mining.


  • Paper submission: 02/07 (NEW DEADLINE: 08/08)
  • Author notification: until 10/08 (NEW DEADLINE: until 13/09)
  • Camera-ready due: 24/08 (NEW DEADLINE: 20/09)



  • Papers may be written in Portuguese or English, but the title, the abstract and the keywords must be written in English.
  • 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.

Papers must be submitted through the web page:

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.

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.

Please follow the submission template. For further inquiries, please contact the Program Committee Chair at the email:


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.


  • 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
  • Recommender Systems based on Data Mining
  • Sequential Patterns
  • Social Network Mining
  • Stream Data Mining
  • Text Mining
  • Time-Series Analysis
  • Visual Data Mining Web Mining


  • Active Learning
  • Bayesian Inference
  • Case-Based Reasoning
  • Cognitive Models of Learning
  • Constructive Induction and Theory Revision
  • Cost-Sensitive Learning
  • Ensemble Methods
  • Evaluation Methodology in Machine Learning
  • Fuzzy Learning Systems
  • Inductive Logic Programming and Relational Learning
  • 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 Learning
  • Reinforcement Learning
  • Semi-Supervised Learning
  • Supervised Learning
  • Unsupervised Learning


André Luis Debiaso Rossi (UNESP) –

Elaine Ribeiro de Faria (UFU) –