Pedro Gonzalez Garcia
Universidad de Jaén
Jaén, Spain
Researcher
Bio
Pedro González García works at the Department of Computer Sciences, Universidad de Jaén. He has been cited by 1,545 publications and is a leading researcher in data mining, particularly in subgroup discovery techniques. His research focuses on developing evolutionary algorithms for complex data mining problems, including big data and data stream mining applications.
His doctoral thesis analyzed in depth the subgroup discovery and emerging pattern mining tasks for the resolution of complex problems, contributing significantly to the field of descriptive data mining using supervised learning techniques.
Research Focus
Subgroup Discovery: He is a leading expert in subgroup discovery (SD), a descriptive data mining technique using supervised learning. His research focuses on developing evolutionary algorithms for SD applications across various domains.
Big Data and Data Streams: His work addresses complex challenges in big data environments and continuous data streams, developing scalable algorithms for real-world applications.
Evolutionary Algorithms: He specializes in applying evolutionary computation techniques to data mining problems, with particular emphasis on multiobjective optimization approaches.
Fuzzy Systems: His research incorporates fuzzy logic and evolutionary fuzzy systems for enhanced pattern discovery and rule extraction in complex datasets.
Key Algorithms and Contributions
MEFASD-BD: Multi-objective evolutionary fuzzy algorithm for subgroup discovery in big data environments - A MapReduce solution (Knowledge-Based Systems)
MEFES: Evolutionary proposal for the detection of exceptions in subgroup discovery with applications to Concentrating Photovoltaic Technology
MOEA-EFEP: Multi-Objective Evolutionary Algorithm for the Extraction of Fuzzy Emerging Patterns
SDEFSR Package: Contributor to the R package for Subgroup Discovery with Evolutionary Fuzzy Systems
Research Applications
- E-learning Data Mining - Applying subgroup discovery to learning management systems
- Environmental Applications - Concentrating Photovoltaic Technology optimization
- Bioinformatics - Pattern discovery in biological datasets
- Big Data Analytics - Scalable algorithms for large-scale data mining
Software Development
- SDEFSR Package for R - “Subgroup Discovery with Evolutionary Fuzzy Systems in R: the SDEFSR Package” (The R Journal)
- MapReduce Solutions - Big data implementations of subgroup discovery algorithms
Research Areas
Data Science | Big Data | Machine Learning | Soft Computing | Evolutionary Algorithms | Fuzzy Logic | Neural Networks | Environmental Applications | Bioinformatics
Interests
Subgroup Discovery | Data Mining | Evolutionary Algorithms | Big Data | Fuzzy Systems