6533b858fe1ef96bd12b6529
RESEARCH PRODUCT
Distributed Data Clustering via Opinion Dynamics
Adriano FagioliniGabriele OlivaDamiano La MannaRoberto Setolasubject
Theoretical computer scienceArticle SubjectComputer Networks and Communicationsbusiness.industryComputer scienceGeneral EngineeringConstrained clusteringPartition (database)lcsh:QA75.5-76.95NETWORKSDetermining the number of clusters in a data setConsensusSettore ING-INF/04 - AutomaticaCONSENSUS PROBLEMSWirelesslcsh:Electronic computers. Computer sciencebusinessCluster analysisdescription
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.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2015-03-01 |