Call for Papers – KDMile

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, in its fourth edition, KDMiLe will be held in Recife, state of Pernambuco, Brazil from October 9 to 11 in conjunction with The Brazilian Conference on Intelligent Systems (BRACIS). 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.

Important Dates
Paper Submission : 20th August, 2016
Authors Notification : 15th September, 2016
Camera Ready due : 22nd September, 2016

Symposium Venue
The former capital of the 17th century Dutch Brazil, Recife was founded in 1537 and stands out as a major tourist attraction, both for its beaches and for its historic sites, dating back to both the Portuguese and the Dutch colonization of the region. The beach of Porto de Galinhas, 60 kilometers (37 mi) south of the city, has been repeatedly awarded the title of best beach in Brazil. The Historic Centre of Olinda, 7 kilometers (4.3 mi) north of the city, was declared a UNESCO World Heritage site in 1982, and both cities' Brazilian Carnival are among the world's most famous.

Topics of Interest (not limited to)

Data Mining Topics:
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

Machine Learning Topics:
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

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