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.

Oliva, G., La Manna, D., Fagiolini, A., Setola, R. (2015). Distributed Data Clustering via Opinion Dynamics. INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS, 2015, 1-13 [10.1155/2015/753102].

Distributed Data Clustering via Opinion Dynamics

LA MANNA, Damiano;FAGIOLINI, Adriano;
2015-01-01

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.
Settore ING-INF/04 - Automatica
Oliva, G., La Manna, D., Fagiolini, A., Setola, R. (2015). Distributed Data Clustering via Opinion Dynamics. INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS, 2015, 1-13 [10.1155/2015/753102].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10447/165146
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