Clustering spatially distributed data is well motivated and especially challenging when communication to a central processing unit is discouraged, e.g., due to power constraints. Distributed clustering schemes are developed in this paper for both deterministic and probabilistic approaches to unsupervised learning. The centralized problem is solved in a distributed fashion by recasting it to a set of smaller local clustering problems with consensus constraints on the cluster parameters. The resulting iterative schemes do not exchange local data among nodes, and rely only on single-hop communications. Performance of the novel algorithms is illustrated with simulated tests on synthetic and real sensor data. Surprisingly, these tests reveal that the distributed algorithms can exhibit improved robustness to initialization than their centralized counterparts.
|Original language||English (US)|
|Number of pages||18|
|Journal||IEEE Journal on Selected Topics in Signal Processing|
|State||Published - Aug 2011|
Bibliographical noteFunding Information:
Manuscript received June 01, 2010; revised November 15, 2010; accepted January 25, 2011. Date of publication February 14, 2011; date of current version July 20, 2011. This work was supported in part by the National Science Foundation (NSF) under Grants CCF 0830480 and CON 014658 and also in part through collaborative participation in the Communications and Networks Consortium sponsored by the U.S. Army Research Laboratory under the Collaborative Technology Alliance Program, Cooperative Agreement DAAD19-01-2-0011. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation thereon. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies of the Army Research Laboratory or the U.S. Government. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Anna Scaglione.
- Clustering methods
- distributed algorithms
- expectation-maximization (EM) algorithms
- iterative methods
- wireless sensor networks