Hierarchical topological clustering
This provides a flexible clustering method for domains with complex data structures and outliers, though it appears incremental as it builds on existing topological approaches.
The authors tackled the problem of clustering data with arbitrary shapes and outliers by proposing a hierarchical topological clustering algorithm that works with any distance metric, demonstrating its effectiveness on image, medical, and economic datasets where other techniques fail.
Topological methods have the potential of exploring data clouds without making assumptions on their the structure. Here we propose a hierarchical topological clustering algorithm that can be implemented with any distance choice. The persistence of outliers and clusters of arbitrary shape is inferred from the resulting hierarchy. We demonstrate the potential of the algorithm on selected datasets in which outliers play relevant roles, consisting of images, medical and economic data. These methods can provide meaningful clusters in situations in which other techniques fail to do so.