As in the last years, KDMiLe comes with two different tracks: Algorithms and Applications.
The KDMiLe Program Committee invites submissions containing algorithms and/or applications in Data Mining and Machine Learning. Submitted papers will be peer-reviewed based on originality, relevance, technical soundness and clarity of presentation.
Authors are encouraged to submit articles containing new ideas, discussions on existing work, practical studies, and experiments relevant to the field of Artificial and Computational Intelligence, which have not been previously published. The topics of interest include, but are not limited to:
IN DATA MINING (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
- 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 (NOT LIMITED TO)
- 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 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
- 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
Please download the template HERE | Camera ready here
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: https://jems.sbc.org.br/
kdmile2022 - Submissions are reviewed following a single blind review process, i.e. you do not need to hide authors’ names and affiliations.
- 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.
- Paper submission: September 11th, 2022 September 18th, 2022
- Notification to authors: October 21st, 2022 October 23rd, 2022
- Camera-ready copy due: October 28th, 2022 October 31st, 2022
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)
Chair – Diego Furtado Silva (UFSCar)
Co-Chair – Aline Marins Paes Carvalho (UFF)