Clustering the results from brainstorm sessions

This research addresses the problem of clustering the results of brainstorm sessions. Going through all the ideas from the brainstorm session and consolidating them through clustering 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 and we identify the different factors that can influence the clustering 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, but the results are influenced by the subjective behaviour of human interpreters.