Anzeige
Erschienen in:
2010 | OriginalPaper | Buchkapitel
Topic Number Estimation by Consensus Soft Clustering with NMF
verfasst von : Takeru Yokoi
Erschienen in: Future Generation Information Technology
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
We propose here a novel method to estimate the number of topics in a document set using consensus clustering based on Non-negative Matrix Factorization (NMF). It is useful to automatically estimate the number of topics from a document set since various approaches to extract topics can determine their number through heuristics. Consensus clustering makes it possible to obtain a consensus of multiple results of clustering so that robust clustering is achieved and the number of clusters is regarded as the optimized number. In this paper, we have proposed a novel consensus soft clustering algorithm based on NMF and estimated an optimized number of topics by searching through a robust classification of documents for the topics obtained.