The Symposium on Knowledge Discovery, Mining and Learning (KDMiLe) is an event held by SBC since 2013 organized alternatively in conjunction with the Brazilian Symposium on Databases (SBBD) and the Brazilian Conference on Intelligent Systems (BRACIS). The 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. The Proceedings of the KDMiLe are published annually, bringing the articles selected for each edition of the event.

Topics of Interest

KDMiLe topics of interest in Data Mining include, but are not limited to:

  • 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

KDMiLe topics of interest in Machine Learning include, but are not limited to:

  • 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

Additional information

For more information about KDMiLe, visit the website of the current edition.