Distributed Data Clustering via Opinion Dynamics. (8th March 2015)

• Record Type: Journal Article
• Authors: Oliva, Gabriele; La Manna, Damiano; Fagiolini, Adriano; Setola, Roberto
• Is Part Of: International journal of distributed sensor networks. Volume 11:Number 3(2015)
• Publication Date: 2015-03-08
• Subjects: Sensor networks -- Periodicals; Intelligent agents (Computer software) -- Periodicals; Multisensor data fusion -- Periodicals; 681.2
• DOI: 10.1155/2015/753102
• Languages: English
• ISSNs: 1550-1329

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.