Estimation of an Optimized Number of Topics by Consensus Soft Clustering Using NMF
作者: Takeru Yokoi
作者单位: 1Tokyo Metropolitan College of Industrial Technology Japan
刊名: Electronics and Communications in Japan, 2013, Vol.96 (8), pp.50-58
来源数据库: Wiley Journal
DOI: 10.1002/ecj.11438
关键词: consensus clusteringsoft clusteringtopic extractiontopic number estimation
原始语种摘要: SUMMARY(#br)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...
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