Visualising Search Result Sets Using a Force-Based Method to Form Clusters of Similar Documents

As human knowledge increases, so the volume of electronically available information grows. Finding specific information becomes more difficult and ever more matches are returned in response to a search query. Since quantity is seldom quality, numerous approaches to make sense of search result sets have been proposed. This thesis describes an approach called SearchVis to visualise search result sets, which is based on an approach by Matthew Chalmers described in his 1996 paper ’A Linear Iterative Layout Algorithm for Visualising High-Dimensional Data’. The visualisation concentrates on the similarities between the documents retrieved. An animated, force-based technique produces clusters of similar documents. Through this technique similar documents are attracted and non-similar documents repelled. SearchVis allows the user to adjust the visual discrimination of the clusters using different parameters. It was tested with a varity of test data sets for a wide range of parameter settings. In order to reach as wide an audience as possible, SearchVis was written in Java.