Dynamic Spectral Clustering

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Please use this identifier to cite or link to this item: http://hdl.handle.net/1853/42615

Title: Dynamic Spectral Clustering
Author: LaViers, Amy ; Rahmani, Amir R. ; Egerstedt, Magnus B.
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
Description: Presented at the 19th International Symposium on Mathematical Theory of Networks and Systems, MTNS 2010, University Congress Center, Budapest, Hungary, July 2010.
Type: Proceedings
URI: http://hdl.handle.net/1853/42615
Citation: LaViers, A. Rahmani, and M. Egerstedt, "Dynamic Spectral Clustering," Mathematical Theory of Networks and Systems, Budapest, Hungary, July 2010.
Date: 2010-07
Contributor: Georgia Institute of Technology. School of Electrical and Computer Engineering
Georgia Institute of Technology. Center for Robotics and Intelligent Machines
Publisher: Georgia Institute of Technology
Subject: Clustering
Data classification
Algorithms
Fixed time interval
Time-aggregated similarity measure

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