NDLI: Fast density-weighted low-rank approximation spectral clustering

Content Provider	Springer Nature Link
Author	Shang, Fanhua Jiao, L. C. Shi, Jiarong Gong, Maoguo Shang, R. H.
Copyright Year	2010
Abstract	While spectral clustering can produce high-quality clusterings on small data sets, computational cost makes it infeasible for large data sets. Affinity Propagation (AP) has a limitation that it is hard to determine the value of parameter ‘preference’ which can lead to an optimal clustering solution. These problems limit the scope of application of the two methods. In this paper, we develop a novel fast two-stage spectral clustering framework with local and global consistency. Under this framework, we propose a Fast density-Weighted low-rank Approximation Spectral Clustering (FWASC) algorithm to address the above issues. The proposed algorithm is a high-quality graph partitioning method, and simultaneously considers both the local and global structure information contained in the data sets. Specifically, we first present a new Fast Two-Stage AP (FTSAP) algorithm to coarsen the input sparse graph and produce a small number of final representative exemplars, which is a simple and efficient sampling scheme. Then we present a density-weighted low-rank approximation spectral clustering algorithm to operate those representative exemplars on the global underlying structure of data manifold. Experimental results show that our algorithm outperforms the state-of-the-art spectral clustering and original AP algorithms in terms of speed, memory usage, and quality.
Starting Page	345
Ending Page	378
Page Count	34
File Format	PDF
ISSN	13845810
Journal	Data Mining and Knowledge Discovery
Volume Number	23
Issue Number	2
e-ISSN	1573756X
Language	English
Publisher	Springer US
Publisher Date	2010-12-04
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Clustering Data mining Affinity propagation Spectral clustering Nyström approximation Manifold structure Computing Methodologies Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences Statistics Data Mining and Knowledge Discovery Information Storage and Retrieval Artificial Intelligence (incl. Robotics)
Content Type	Text
Resource Type	Article
Subject	Computer Networks and Communications Information Systems Computer Science Applications

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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