A fast incremental spectral clustering for large data sets
Date
2011
Authors
Kong, T.
Tian, Y.
Shen, H.
Editors
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Conference paper
Citation
Proceedings of the 12th International Conference on Parallell and Distributed Computing, Applications and Technologies, held in Gwangju, South Korea, 20-22 October, 2011: pp.1-5
Statement of Responsibility
Tengteng Kong, Ye Tian, Hong Shen
Conference Name
International Conference on Parallel and Distributed Computing, Applications and Technologies (12th : 2011 : Gwangju, South Korea)
Abstract
Spectral clustering is an emerging research topic that has numerous applications, such as data dimension reduction and image segmentation. In spectral clustering, as new data points are added continuously, dynamic data sets are processed in an on-line way to avoid costly re-computation. In this paper, we propose a new representative measure to compress the original data sets and maintain a set of representative points by continuously updating Eigen-system with the incidence vector. According to these extracted points we generate instant cluster labels as new data points arrive. Our method is effective and able to process large data sets due to its low time complexity. Experimental results over various real evolutional data sets show that our method provides fast and relatively accurate results.
School/Discipline
Dissertation Note
Provenance
Description
Access Status
Rights
© 2011 IEEE