Title:
Dynamic Spectral Clustering

dc.contributor.author LaViers, Amy en_US
dc.contributor.author Rahmani, Amir R. en_US
dc.contributor.author Egerstedt, Magnus B. en_US
dc.contributor.corporatename Georgia Institute of Technology. School of Electrical and Computer Engineering en_US
dc.contributor.corporatename Georgia Institute of Technology. Center for Robotics and Intelligent Machines en_US
dc.date.accessioned 2012-02-13T20:46:46Z
dc.date.available 2012-02-13T20:46:46Z
dc.date.issued 2010-07
dc.description Presented at the 19th International Symposium on Mathematical Theory of Networks and Systems, MTNS 2010, University Congress Center, Budapest, Hungary, July 2010. en_US
dc.description.abstract Clustering is a powerful tool for data classification; however, its application has been limited to analysis of static snapshots of data which may be time-evolving. This work presents a clustering algorithm that employs a fixed time interval and a time-aggregated similarity measure to determine classification. The fixed time interval and a weighting parameter are tuned to the system’s dynamics; otherwise the algorithm proceeds automatically finding the optimal cluster number and appropriate clusters at each time point in the dataset. The viability and contribution of the method is shown through simulation en_US
dc.identifier.citation LaViers, A. Rahmani, and M. Egerstedt, "Dynamic Spectral Clustering," Mathematical Theory of Networks and Systems, Budapest, Hungary, July 2010. en_US
dc.identifier.uri http://hdl.handle.net/1853/42615
dc.language.iso en_US en_US
dc.publisher Georgia Institute of Technology en_US
dc.subject Clustering en_US
dc.subject Data classification en_US
dc.subject Algorithms en_US
dc.subject Fixed time interval en_US
dc.subject Time-aggregated similarity measure en_US
dc.title Dynamic Spectral Clustering en_US
dc.type Text
dc.type.genre Proceedings
dspace.entity.type Publication
local.contributor.author Egerstedt, Magnus B.
local.contributor.corporatename School of Electrical and Computer Engineering
local.contributor.corporatename College of Engineering
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relation.isOrgUnitOfPublication 7c022d60-21d5-497c-b552-95e489a06569
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