Publicação

Comparing clustering and partitioning strategies

Detalhes bibliográficos
Resumo:	In this work we compare balance and edge-cut evaluation metrics to measure the performance of two well-known graph data-grouping algorithms applied to four web and social network graphs. One of the algorithms employs a partitioning technique using Kmetis tool, and the other employs a clustering technique using Scluster tool. Because clustering algorithms use a similarity measure between each graph node and partitioning algorithms use a dissimilarity measure (weight), it was necessary to apply a normalized function to convert weighted graphs to similarity matrices. The numerical results show that partitioning algorithms behave clearly better than to the clustering counterparts when applied to these types of graphs.
Autores principais:	Afonso, Carlos
Outros Autores:	Ferreira, Fábio; Exposto, José; Pereira, Ana I.
Assunto:	Clustering Partitioning Web graph
Ano:	2012
País:	Portugal
Tipo de documento:	comunicação em conferência
Tipo de acesso:	acesso aberto
Instituição associada:	Instituto Politécnico de Bragança
Idioma:	inglês
Origem:	Biblioteca Digital do IPB

Descrição
Resumo:	In this work we compare balance and edge-cut evaluation metrics to measure the performance of two well-known graph data-grouping algorithms applied to four web and social network graphs. One of the algorithms employs a partitioning technique using Kmetis tool, and the other employs a clustering technique using Scluster tool. Because clustering algorithms use a similarity measure between each graph node and partitioning algorithms use a dissimilarity measure (weight), it was necessary to apply a normalized function to convert weighted graphs to similarity matrices. The numerical results show that partitioning algorithms behave clearly better than to the clustering counterparts when applied to these types of graphs.