A Parameter-free Adaptive Resonance Theory-based Topological Clustering Algorithm Capable of Continual Learning
This work addresses the need for robust, adaptive clustering algorithms in machine learning, offering an incremental improvement by automating parameter estimation in ART-based methods.
The paper tackles the problem of parameter sensitivity in Adaptive Resonance Theory-based clustering by proposing a parameter-free algorithm that automatically estimates similarity and edge deletion thresholds, achieving superior clustering performance compared to state-of-the-art methods on synthetic and real-world datasets without dataset-specific parameter tuning.
In general, a similarity threshold (i.e., a vigilance parameter) for a node learning process in Adaptive Resonance Theory (ART)-based algorithms has a significant impact on clustering performance. In addition, an edge deletion threshold in a topological clustering algorithm plays an important role in adaptively generating well-separated clusters during a self-organizing process. In this paper, we propose an ART-based topological clustering algorithm that integrates parameter estimation methods for both the similarity threshold and the edge deletion threshold. The similarity threshold is estimated using a determinantal point process-based criterion, while the edge deletion threshold is defined based on the age of edges. Experimental results with synthetic and real-world datasets show that the proposed algorithm has superior clustering performance to state-of-the-art clustering algorithms without requiring parameter specifications specific to the datasets. Source code is available at https://github.com/Masuyama-lab/CAE