Parallel Spectral Clustering in Distributed Systems

Spectral clustering algorithms have been shown to be more effective in finding clusters than some traditional algorithms, such as k-means. However, spectral clustering suffers from a scalability problem in both memory use and computational time when the size of a data set is large. To perform cluste...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	IEEE transactions on pattern analysis and machine intelligence 2011-03, Vol.33 (3), p.568-586
Hauptverfasser:	Chen, Wen-Yen, Song, Yangqiu, Bai, Hongjie, Lin, Chih-Jen, Chang, Edward Y.
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Applied sciences Approximation Artificial Intelligence Cluster Analysis Clustering Clustering algorithms Computation Computer Communication Networks - instrumentation Computer science Computer science control theory systems Computer Simulation Computer systems and distributed systems. User interface Concurrent computing Data processing. List processing. Character string processing Distributed computing Exact sciences and technology Information retrieval. Graph Laplace equations Memory organisation. Data processing Models, Statistical Nearest neighbor searches nearest neighbors normalized cuts Nyström approximation Parallel algorithms Parallel processing Parallel spectral clustering Pattern Recognition, Automated - methods Reproducibility of Results Scalability Similarity Software Sparse matrices Spectra Strategy Systems Integration Theoretical computing USA Councils
Online-Zugang:	Volltext bestellen
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!