Distributed Data Clustering via Opinion Dynamics

Damiano La Manna, Adriano Fagiolini, Gabriele Oliva, Roberto Setola

Risultato della ricerca: Articlepeer review

6 Citazioni (Scopus)

Abstract

We provide a distributed method to partition a large set of data in clusters, characterized by small in-group and large out-group distances. We assume a wireless sensors network in which each sensor is given a large set of data and the objective is to provide a way to group the sensors in homogeneous clusters by information type. In previous literature, the desired number of clusters must be specified a priori by the user. In our approach, the clusters are constrained to have centroids with a distance at least ε between them and the number of desired clusters is not specified. Although traditional algorithms fail to solve the problem with this constraint, it can help obtain a better clustering. In this paper, a solution based on the Hegselmann-Krause opinion dynamics model is proposed to find an admissible, although suboptimal, solution. The Hegselmann-Krause model is a centralized algorithm; here we provide a distributed implementation, based on a combination of distributed consensus algorithms. A comparison with k-means algorithm concludes the paper.
Lingua originaleEnglish
pagine (da-a)1-13
Numero di pagine13
RivistaInternational Journal of Distributed Sensor Networks
Volume2015
Stato di pubblicazionePublished - 2015

All Science Journal Classification (ASJC) codes

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  • Computer Networks and Communications

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