Topic Detection using fuzzy c-means with nonnegative double singular value decomposition initialization

Hamimah Alatas, Hendri Murfi, Alhadi B.

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)206-222
Number of pages17
JournalInternational Journal of Advances in Soft Computing and its Applications
Volume10
Issue number2
Publication statusPublished - 1 Jan 2018

Keywords

  • Fuzzy c-means
  • Initialization
  • Singular value decomposition
  • Topic detection
  • Topic modeling
  • Twitter

Fingerprint Dive into the research topics of 'Topic Detection using fuzzy c-means with nonnegative double singular value decomposition initialization'. Together they form a unique fingerprint.

Cite this