Use of meta-learning for clustering algorithm selection problems

• Grant number: 17/20265-0
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• Start date: November 01, 2017
• End date: July 28, 2019
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
• Principal Investigator: André Carlos Ponce de Leon Ferreira de Carvalho
• Grantee: Bruno Almeida Pimentel
• Host Institution: Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil
• Associated research grant: 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry, AP.CEPID

Abstract

Clustering is one of the main Machine Learning tasks. There are several data clustering techniques and different problems are different techniques. Choosing an algorithm in a non-automated way can be costly and requires in-depth knowledge of the problem and algorithms. In this way, meta-learning emerges as a tool to automate the process of algorithm selection. The proposal of this project is to investigate a methodology for automatic selection of algorithms in data clustering problems using meta-learning.

Scientific publications

(6)

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

PIMENTEL, BRUNO ALMEIDA; DE CARVALHO, ANDRE C. P. L. E.; IEEE. Statistical versus Distance-Based Meta-Features for Clustering Algorithm recommendation Using Meta-Learning. 2018 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), v. N/A, p. 8-pg., 2018-01-01. (12/22608-8, 17/20265-0, 16/18615-0)

PIMENTEL, BRUNO ALMEIDA; DE CARVALHO, ANDRE C. P. L. E.; IEEE. Unsupervised Meta-Learning for Clustering Algorithm Recommendation. 2019 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), v. N/A, p. 8-pg., 2019-01-01. (12/22608-8, 17/20265-0, 16/18615-0)

ALCOBACA, EDESIO; MASTELINI, SAULO MARTIELLO; BOTARI, TIAGO; PIMENTEL, BRUNO ALMEIDA; CASSAR, DANIEL ROBERTO; DE LEON FERREIRA DE CARVALHO, ANDRE CARLOS PONCE; ZANOTTO, EDGAR DUTRA. Explainable Machine Learning Algorithms For Predicting Glass Transition Temperatures. ACTA MATERIALIA, v. 188, p. 92-100, APR 15 2020. (17/12491-0, 13/07375-0, 18/07319-6, 17/06161-7, 17/20265-0, 13/07793-6, 18/14819-5)

PIMENTEL, BRUNO ALMEIDA; DE CARVALHO, ANDRE C. P. L. F.. A Meta-learning approach for recommending the number of clusters for clustering algorithms. KNOWLEDGE-BASED SYSTEMS, v. 195, MAY 11 2020. (16/18615-0, 17/20265-0, 12/22608-8)

CAVALCANTI, RODRIGO B. DE C.; PIMENTEL, BRUNO A.; DE ALMEIDA, CARLOS W. D.; DE SOUZA, RENATA M. C. R.; IEEE. A Multivariate Fuzzy Kohonen Clustering Network. 2019 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), v. N/A, p. 7-pg., 2019-01-01. (17/20265-0)

PIMENTEL, BRUNO ALMEIDA; DE CARVALHO, ANDRE C. P. L. F.. A new data characterization for selecting clustering algorithms using meta-learning. INFORMATION SCIENCES, v. 477, p. 203-219, MAR 2019. (13/07375-0, 16/18615-0, 17/20265-0)