L2GClust: local-to-global clustering of stream sources

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2011

Autores

Rodrigues, PP (Autor) FMUP; Gama, J (Autor) FEP; Araujo, J (Autor) Outra; Lopes, L (Autor) FCUP

Ata de Conferência Internacional

Título: Proceedings of the ACM Symposium on Applied Computing

Páginas: 1006-1011

26th Annual ACM Symposium on Applied Computing, SAC 2011

TaiChung, 21 March 2011 through 24 March 2011

Indexação

ISI Web of Knowledge

Scopus - 3 Citações

Classificação Científica

CORDIS: Ciências Físicas > Ciência de computadores

Outras Informações

ID Authenticus: P-00H-Z2P

DOI: 10.1145/1982185.1982405

Abstract (EN): In ubiquitous streaming data sources, such as sensor networks, clustering nodes by the data they produce is an important problem that gives insights on the phenomenon being monitored by such networks. However, if these techniques require data to be gathered centrally, communication and storage requirements are often unbounded. The goal of this paper is to assess the feasibility of computing local clustering at each node, using only neighbors' centroids, as an approximation of the global clustering computed by a centralized process. A local algorithm is proposed to perform clustering of sensors based on the moving average of each node's data over time: the moving average of each node is approximated using memory-less fading average; clustering is based on the furthest point algorithm applied to the centroids computed by the node's direct neighbors. The algorithm was evaluated on a state-of-the-art sensor network simulator, measuring the agreement between local and global clustering. Experimental work on synthetic data with spherical Gaussian clusters is consistently analyzed for different network size, number of clusters and cluster overlapping. Results show a high level of agreement between each node's clustering definitions and the global clustering definition, with special emphasis on separability agreement. Overall, local approaches are able to keep a good approximation of the global clustering, improving privacy among nodes, and decreasing communication and computation load in the network. Hence, the basic requirements for distributed clustering of streaming data sensors recommend that clustering on these settings should be performed locally. © 2011 ACM.

Idioma: Inglês

Tipo (Avaliação Docente): Científica