Cluster-based and association-based visualization systems as information exploration tools

Abstract

The main purpose of this study is to investigate whether two of the algorithms, variance of Ward's hierarchical clustering algorithm and a Kohonen neural network algorithm, can help improve information exploration of unknown data collections. The initial results of the study indicate that both BiblioMapper-based and Kohonen SOM-based algorithms can successfully categorize heterogeneous data collections into manageable sub-spaces that users can successfully navigate to locate a document of interest. Both BiblioMapper and Kohonen SOM worked best with browsing tasks that were very broad, in which subjects skipped around between categories. Subjects who preferred keyword search and those who wanted to use the more familiar mental models (alphabetical organization) for browsing found that the map did not work well.