Adaptive Resonance Theory-Based Topological Clustering with Node Deletion Mechanism for Evolving Stream Data
Takanori Takebayashi, Naoki Masuyama, Yusukeusuke Nijima · 2023
Concept drift where the data distribution changes over time deteriorates the performance of machine learning models. Adaptive Resonance Theory (ART)-based clustering algorithms are capable of learning continually and achieving fast and stable clustering. However, concept drift has not been actively discussed in ART-based clustering algorithms. In this paper, we propose an ART-based clustering algorithm that can deal with concept drift. Through computational experiments, we confirmed the performance of the proposed algorithm in synthetic and real-world datasets.