Visualization tools for parameter selection in cluster analysis
This addresses the challenge of parameter selection for researchers and practitioners in data analysis, but it appears incremental as it builds on existing clustering visualization methods.
The paper tackles the problem of visualizing parameter selection in cluster analysis by proposing HPREF, an algorithm that creates a hierarchical partition of clusterings from a fixed dataset, providing geometric structure to visualize results from running a clustering algorithm with varying parameters.
We propose an algorithm, HPREF (Hierarchical Partitioning by Repeated Features), that produces a hierarchical partition of a set of clusterings of a fixed dataset, such as sets of clusterings produced by running a clustering algorithm with a range of parameters. This gives geometric structure to such sets of clustering, and can be used to visualize the set of results one obtains by running a clustering algorithm with a range of parameters.