A traffic-based evolutionary algorithm for network clustering

Abstract

a b s t r a c tNetwork clustering algorithms are typically based only on the topology information of the network. In this paper, we introduce traffic as a quantity representing the intensity of the relationship among nodes in the network, regardless of their connectivity, and propose an evolutionary clustering algorithm, based on the application of genetic operators and capable of exploiting the traffic information. In a comparative evaluation based on synthetic instances and two real world datasets, we show that our approach outperforms a selection of well established evolutionary and non-evolutionary clustering algorithms.

Weighted ensemble learning of Bayesian network for gene regulatory networks

“…Other surveys could be found in Alizadeh et al [3] and Ghosh and Acharya [29]. The authors of Naldi et al [49] propose various strategies to rank the partitions of a partition set and include or exclude them from composing the consensus partition. The rank is based on the internal clustering validation indexes [41] and on external validation indexes measuring the dissimilarity between couples of partitions.…”

Análise exploratória de método utilizando <i>Wavelet</i> para detecção de padrões e anomalias em dados históricos do tráfego veicular

“…Engenharia de Transportes(CHUNG, 2003; WEIJERMARS; BERKUM, 2005; THOMAS; WEIJERMARS; BERKUM, 2008), outros trabalhos utilizaram esse tipo de análise para classificação de dadosSETTI, 2008; ZHU et al, 2016), economia de transportes(KUMAR et al, 2016), prevenção de acidentes(OÑA et al, 2016) e Sistema de Informação de Tráfego(ARKIAN et al, 2014;NALDI et al, 2013).Para a aplicação da análise de cluster é necessário definir a medida de similaridade a ser calculada nas variáveis e o algoritmo de agrupamento, para então realizar e interpretar os agrupamentos. Similaridade é a medida de correspondência ou semelhança entre os objetos a serem agrupados.…”