The K-means with mini batch algorithm for topics detection on online news

Siti Rofiqoh Fitriyani, Hendri Murfi

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

13 Citations (Scopus)

Abstract

Online media is the most important media for accessing a wide range of information, such as news. Nowadays, there are many news agencies publish digital news via online media. The popularity of the online news makes the increasing volume of available news. This leads to the necessity of automated methods for news analysis, i.e.Topics detection. One of The topic detection methods is the K-means algorithm. However, this algorithm is slow for big datasets. Therefore, the mini batch approach is used to reduce the computational time. Our experiments show that the computational times of the mini batch approach is much faster for the comparable accuracies.

Original languageEnglish
Title of host publication2016 4th International Conference on Information and Communication Technology, ICoICT 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781467398794
DOIs
Publication statusPublished - 19 Sep 2016
Event4th International Conference on Information and Communication Technology, ICoICT 2016 - Bandung, Indonesia
Duration: 25 May 201627 May 2016

Publication series

Name2016 4th International Conference on Information and Communication Technology, ICoICT 2016

Conference

Conference4th International Conference on Information and Communication Technology, ICoICT 2016
Country/TerritoryIndonesia
CityBandung
Period25/05/1627/05/16

Keywords

  • K-means algorithm
  • mini batch
  • online news
  • topic detection

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