A Meta-learning approach for recommending the number of clusters for clustering algorithms

Autor(es)

Pimentel, Bruno Almeida ^[1] ; de Carvalho, Andre C. P. L. F. ^[1]

Afiliação do(s) autor(es)

^[1] Univ Sao Paulo, Inst Ciencias Matemat & Comp, Sao Carlos - Brazil

• Tipo de documento: Artigo Científico
• Fonte: KNOWLEDGE-BASED SYSTEMS; v. 195, MAY 11 2020.
• Citações Web of Science: 0

Resumo

One of the main challenges in Clustering Analysis is choosing the optimal number of clusters. A typical methodology is to evaluate a validity index over the data and to optimize it as a function of the number of clusters. However, this process can have a high computational cost. In this work, we introduce a new approach for recommending the number of clusters for a particular dataset by using Meta-learning. As the predictive performance of the meta-models induced by Meta-learning is affected by how datasets are described by meta-features, we propose a new set of meta-features able to improve the predictive performance of meta-models used for recommending the number of clusters. Experimental results show that the proposed approach provides a good recommendation of the number of clusters. Additionally, the proposed meta-feature obtains better results than meta-features for clustering tasks found in the literature. (C) 2020 Elsevier B.V. All rights reserved. (AU)

• Processo FAPESP: 16/18615-0 - Aprendizado de máquina avançado
• Beneficiário: André Carlos Ponce de Leon Ferreira de Carvalho
• Modalidade de apoio: Auxílio à Pesquisa - Parceria para Inovação Tecnológica - PITE
• Processo FAPESP: 17/20265-0 - Uso de meta-aprendizado para seleção de algoritmos em problemas de agrupamento
• Beneficiário: Bruno Almeida Pimentel
• Modalidade de apoio: Bolsas no Brasil - Pós-Doutorado
• Processo FAPESP: 12/22608-8 - Uso de medidas de complexidade de dados no suporte ao aprendizado de máquina supervisionado
• Beneficiário: Ana Carolina Lorena
• Modalidade de apoio: Auxílio à Pesquisa - Jovens Pesquisadores