Kernelized Eigenspace based fuzzy C-means for sensing trending topics on twitter

Yudho Prakoso, Hendri Murfi, Arie Wibowo

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

1 Citation (Scopus)

Abstract

One of the automated methods for textual data analysis is topic detection. Eigenspace-based fuzzy c-means (EFCM) is a soft clustering-based method for topic detection. Firstly, EFCM uses truncated singular value decomposition to transform high dimensional textual data into low dimensional textual data. Next, the clustering process is conducted in the lower dimensional space. However, that transformation process may eliminate some important features from the textual data. Therefore, the accuracy may be reduced. In this paper, we use kernel trick to overcome that weakness so that the clustering process is performed in a higher dimensional space without explicitly transforming the textual data to space. Our simulations show that this approach improves the accuracies of EFCM in term of topic recall for the problem of sensing trending topic on Twitter.

Original languageEnglish
Title of host publicationProceedings of the 2018 International Conference on Data Science and Information Technology, DSIT 2018
PublisherAssociation for Computing Machinery
Pages6-10
Number of pages5
ISBN (Electronic)9781450365215
DOIs
Publication statusPublished - 20 Jul 2018
Event2018 International Conference on Data Science and Information Technology, DSIT 2018 - Singapore, Singapore
Duration: 20 Jul 201822 Jul 2018

Publication series

NameACM International Conference Proceeding Series

Conference

Conference2018 International Conference on Data Science and Information Technology, DSIT 2018
CountrySingapore
CitySingapore
Period20/07/1822/07/18

Keywords

  • Clustering
  • Fuzzy C-Means
  • Kernel Trick
  • Singular Value Decomposition
  • Topic Detection
  • Topic Modeling

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