Clustering the Results of Brainstorm Sessions: Applying Word Similarity Techniques to Cluster Dutch Nouns

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Abstract

This research addresses the problem of clustering the results of brainstorm sessions. Going through all ideas and clustering them can be a time consuming task. In this research we design a computer-aided approach that can help with clustering of these results. We have limited ourselves to looking at single words, while at the same time we identify the different factors that could influence the results. These factors are: (1) word similarity algorithm, (2) dimensionality, (3) cluster count, (4) clustering algorithm, and (5) the evaluation approach. In total we tested six word similarity algorithms, two clustering techniques and three evaluation methods, in order to see which configuration works best for the task. We found evidence that the clustering of these results is feasible, though the results are influenced by the subjective behaviour of human interpreters.
Original languageEnglish
Title of host publication2016 49th Hawaii International Conference on System Sciences (HICSS)
Place of PublicationKauai, Hawaii
PublisherIEEE Computer Society
Pages4232-4241
ISBN (Print)978-0-7695-5670-3
DOIs
Publication statusPublished - 5 Jan 2016
Event49th Hawaii International Conference on System Sciences, HICSS 2016 - Kauai, United States
Duration: 5 Jan 20168 Jan 2016
Conference number: 49

Publication series

Name
PublisherIEEE Computer Society

Conference

Conference49th Hawaii International Conference on System Sciences, HICSS 2016
Abbreviated titleHICSS
CountryUnited States
CityKauai
Period5/01/168/01/16

Keywords

  • IR-100189
  • METIS-316359

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  • Cite this

    Amrit, C. A., & Hek, J. (2016). Clustering the Results of Brainstorm Sessions: Applying Word Similarity Techniques to Cluster Dutch Nouns. In 2016 49th Hawaii International Conference on System Sciences (HICSS) (pp. 4232-4241). Kauai, Hawaii: IEEE Computer Society. https://doi.org/10.1109/HICSS.2016.526