Unsupervised clustering analysis: A multiscale complex networks approach

Identificador:  imarina:9285210

Handle: https://hdl.handle.net/20.500.11797/imarina9285210

Autores:  Granell, Clara; Gomez, Sergio; Arenas, Alex

Resumen:
Unsupervised clustering, also known as natural clustering, stands for the classification of data according to their similarities. Here we study this problem from the perspective of complex networks. Mapping the description of data similarities to graphs, we propose to extend two multiresolution modularity based algorithms to the finding of modules (clusters) in general data sets producing a multiscales' solution. We show the performance of these reported algorithms to the classification of a standard benchmark of data clustering and compare their performance. © 2012 World Scientific Publishing Company.