文章基本信息

标题：Topic Detection using Fuzzy C-Means with Nonnegative Double Singular Value Decomposition Initialization
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作者：Hamimah Alatas ; Hendri Murfi ; Alhadi Bustamam 等
期刊名称：International Journal of Advances in Soft Computing and Its Applications
印刷版ISSN：2074-8523
出版年度：2018
卷号：10
期号：2
出版社：International Center for Scientific Research and Studies
摘要：Topic Detection or topic modeling is a process of finding topics in a collection of textual data. Detecting topic for a very large document collection hardly done manually. Therefore, we need an automatic method, one of which is a clustering-based method such as fuzzy c-means (FCM). The standard initialization method of FCM is a random initialization which usually produces different topics for each execution. In this paper, we examine a nonrandom initialization method called nonnegative double singular value decomposition (NNDSVD). Besides the advantage of non-randomness, our simulations show that the NNDSVD method gives better accuracies in term of topic recall than both random method and another existing singular value decomposition-based method for the problem of sensing trending topic on Twitter.
关键词：Topic detection; topic modeling; fuzzy c-means; initialization; singular value decomposition; Twitter