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Distributed Data Clustering via Opinion Dynamics

Author

Listed:
  • Gabriele Oliva
  • Damiano La Manna
  • Adriano Fagiolini
  • Roberto Setola

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.

Suggested Citation

  • Gabriele Oliva & Damiano La Manna & Adriano Fagiolini & Roberto Setola, 2015. "Distributed Data Clustering via Opinion Dynamics," International Journal of Distributed Sensor Networks, , vol. 11(3), pages 753102-7531, March.
    • Handle: RePEc:sae:intdis:v:11:y:2015:i:3:p:753102
    DOI: 10.1155/2015/753102
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