Hierarchical Data Generator based on Tree-Structured Stick Breaking Process for Benchmarking Clustering Methods
This work addresses a gap for researchers in hierarchical clustering by providing a tool for benchmarking, though it is incremental as it focuses on data generation rather than new clustering algorithms.
The paper tackles the lack of tools for systematic analysis of Object Cluster Hierarchies by proposing a synthetic hierarchical data generator for benchmarking clustering methods, and it provides publicly available datasets and a generator capable of producing a wide range of differently structured data.
Object Cluster Hierarchies is a new variant of Hierarchical Cluster Analysis that gains interest in the field of Machine Learning. Being still at an early stage of development, the lack of tools for systematic analysis of Object Cluster Hierarchies inhibits its further improvement. In this paper we address this issue by proposing a generator of synthetic hierarchical data that can be used for benchmarking Object Cluster Hierarchy methods. The article presents a thorough empirical and theoretical analysis of the generator and provides guidance on how to control its parameters. Conducted experiments show the usefulness of the data generator that is capable of producing a wide range of differently structured data. Further, benchmarking datasets that mirror the most common types of hierarchies are generated and made available to the public, together with the developed generator (http://kio.pwr.edu.pl/?page\_id=396).