Distributed Data Clustering via Opinion Dynamics | Zendy

Gabriele Oliva | Zendy; D. La Manna | Zendy; Adriano Fagiolini | Zendy; Roberto Setola | Zendy

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Distributed Data Clustering via Opinion Dynamics

Author(s) -

Gabriele Oliva,

D. La Manna,

Adriano Fagiolini,

Roberto Setola

Publication year - 2015

Publication title -

international journal of distributed sensor networks

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.324

H-Index - 53

eISSN - 1550-1477

pISSN - 1550-1329

DOI - 10.1155/2015/753102

Subject(s) - computer science , cluster analysis , partition (number theory) , a priori and a posteriori , centroid , constraint (computer aided design) , wireless sensor network , set (abstract data type) , constrained clustering , distributed algorithm , data mining , algorithm , correlation clustering , artificial intelligence , distributed computing , canopy clustering algorithm , mathematics , computer network , philosophy , geometry , epistemology , combinatorics , programming language

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.

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