Distributed Data Clustering via Opinion Dynamics

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

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)1-13
Number of pages13
JournalInternational Journal of Distributed Sensor Networks
Publication statusPublished - 2015

All Science Journal Classification (ASJC) codes

  • General Engineering
  • Computer Networks and Communications


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