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ICASSP
2010
IEEE

Convergence analysis of consensus-based distributed clustering

13 years 3 months ago
Convergence analysis of consensus-based distributed clustering
This paper deals with clustering of spatially distributed data using wireless sensor networks. A distributed low-complexity clustering algorithm is developed that requires one-hop communications among neighboring nodes only, without local data exchanges. The algorithm alternates iterations over the variables of a consensus-based version of the global clustering problem. Using stability theory for time-varying and timeinvariant systems, the distributed clustering algorithm is shown to be bounded-input bounded-output stable with an output arbitrarily close to a fixed point of the algorithm. For distributed hard K-means clustering, convergence to a local minimum of the centralized problem is guaranteed. Numerical examples confirm the merits of the algorithm and its stability analysis.
Pedro A. Forero, Alfonso Cano, Georgios B. Giannak
Added 06 Dec 2010
Updated 06 Dec 2010
Type Conference
Year 2010
Where ICASSP
Authors Pedro A. Forero, Alfonso Cano, Georgios B. Giannakis
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