Kernelization of eigenspace-based fuzzy C-Means for topic detection on Indonesian news

Mukti Ari, Hendri Murfi

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

Abstract

Topic detection is practical methods to find a topic in a collection of documents. One of the methods is a clustering-based method whose centroids are interpreted as topics, i.e., eigenspace-based fuzzy c-means (EFCM). The clustering process of the EFCM method is performed in a smaller dimensional Eigenspace. Thus, the accuracy of the clustering process may be reduced. In this paper, we use the kernel method so that the clustering process is performed in a higher dimensional space without transforming data into that space. Our simulations show that this kernelization improves the accuracies of EFCM in term of interpretability scores for Indonesian news.

Original languageEnglish
Title of host publication2018 6th International Conference on Information and Communication Technology, ICoICT 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages520-525
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

  • Clustering
  • Eigenspace
  • Fuzzy c-means
  • Kernel
  • Topic detection

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