Paper Info
Title
Clustering The Results Of Brainstorm Sessions: Applying Word Similarity Techniques To Cluster Dutch Nouns
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.
Year
DOI
Venue
2016
10.1109/HICSS.2016.526
PROCEEDINGS OF THE 49TH ANNUAL HAWAII INTERNATIONAL CONFERENCE ON SYSTEM SCIENCES (HICSS 2016)
Keywords
Field
DocType
Word similarity, Word Clustering, Brainstorm
Fuzzy clustering,Clustering high-dimensional data,Correlation clustering,Computer science,Noun,Consensus clustering,Natural language processing,Artificial intelligence,Conceptual clustering,Brown clustering,Cluster analysis,Machine learning
Conference
ISSN
Citations 
PageRank 
1060-3425
0
0.34
References 
Authors
22
2
Name
Order
Citations
PageRank
chintan amrit116019.11
Jeroen Hek200.34