Analysis of non-negative double singular value decomposition initialization method on eigenspace-based fuzzy C-Means algorithm for Indonesian online news topic detection

Raden Trivan Sutrisman, Hendri Murfi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

The rapid increasing of online news in Indonesia creates the need for news analysis to obtain information as fast as possible. Topics are basic components that are often used to analyze data in the textual forms, such as the news article. By using topic modeling, topics can be detected automatically on large news documents which are difficult to perform manually. One of the topic modeling that can be used is the clustering-based method, i.e., Eigenspace-based Fuzzy C-Means (EFCM). The common initialization method of EFCM is random. However, this random initialization usually produces different topics for each run. Therefore, we consider Non-Negative Double Singular Value Decomposition (NNDSVD) as an initialization method of EFCM. Besides the advantage of non-randomness, our simulations show that the NNDSVD method gives better accuracies in term of interpretability score than the random method.

Original languageEnglish
Title of host publication2018 6th International Conference on Information and Communication Technology, ICoICT 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages55-60
Number of pages6
ISBN (Electronic)9781538645710
DOIs
Publication statusPublished - 8 Nov 2018
Event6th International Conference on Information and Communication Technology, ICoICT 2018 - Bandung, Indonesia
Duration: 3 May 20184 May 2018

Publication series

Name2018 6th International Conference on Information and Communication Technology, ICoICT 2018

Conference

Conference6th International Conference on Information and Communication Technology, ICoICT 2018
CountryIndonesia
CityBandung
Period3/05/184/05/18

Keywords

  • Eigenspace
  • Fuzzy c-means
  • Initialization
  • Topic detection

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