Abstract
SUMMARY We propose here a novel approach to exploring an optimized number of topics in a document set using consensus clustering based on nonnegative matrix factorization (NMF). It is useful to automatically determine the number of topics in a document set because various approaches to heuristic topic extraction determine it. Consensus clustering merges multiple results of clustering so as to achieve robust clustering. In this paper, assuming that robust clustering is achieved by optimizing the number of clusters, we propose a novel consensus soft clustering algorithm based on NMF and estimate an optimized number of topics with exploration of a robust classification of documents into topics. © 2013 Wiley Periodicals, Inc. Electron Comm Jpn, 96(8): 50–58, 2013; Published online in Wiley Online Library (wileyonlinelibrary.com). DOI 10.1002/ecj.11438
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: IEEJ Transactions on Electronics, Information and Systems
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
