期刊名称：International Journal of Distributed Sensor Networks
印刷版ISSN：1550-1329
电子版ISSN：1550-1477
出版年度：2015
卷号：2015
DOI：10.1155/2015/753102
出版社：Hindawi Publishing Corporation
摘要：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 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 -means algorithm concludes
the paper.
