Data Pattern Analysis

Research lines:

• Active, inductive and transductive learning
• Anomaly / outlier detection
• Bioinformatics
• Complex networks
• Data cluster analysis (clustering)
• Data mining: structured, unstructured and mixed data
• Data mining: unsupervised, semi-supervised and supervised
• Data preparation:imputation, feature selection, dimensionality reduction, etc.
• Data streams mining
• Ensembles: classifiers, clusterings and outlier detectors
• Graph and text mining
• Machine learning: unsupervised, semi-supervised and supervised
• Nonlinear regression
• One class learning
• Parallel and distributed data mining
• Pattern classification
• Recommender systems
• Social network analysis
• Statistical learning: Bayesian methods
• Transfer learning

Data Patern Analysis
Data Pattern Analysis laboratory
 

Integrantes

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Francisco Aparecido Rodrigues

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