Clustering algorithms can flexibly extract useful knowledge from input data. Therefore, clustering algorithms are often applied to data preprocessing such as dimensionality reduction and feature extraction. Clustering algorithms can also be applied to classifiers thanks to a good knowledge extraction ability. As a conventional study of applying clustering algorithms to classifier design, the algorithm has been proposed that explicitly learns decision boundaries by applying a clustering algorithm to each class of data. However, there are some problems such as instability of learning and slow processing. In this study, we propose a classifier by utilizing the Fast Topological CIM-based Adaptive Resonance Theory (FTCA) that achieves both excellent self-organization performance and high-speed learning. Our experimental results in this paper show that the proposed algorithm has better classification performance compared to other clustering-based classifiers.

This paper proposes a supervised classification algorithm capable of continual learning by utilizing an Adaptive Resonance Theory (ART)-based growing self-organizing clustering algorithm. The ART-based clustering algorithm is theoretically capable of continual learning, and the proposed algorithm independently applies it to each class of training data for generating classifiers. Whenever an additional training data set from a new class is given, a new ART-based clustering will be defined in a different learning space. Thanks to the above-mentioned features, the proposed algorithm realizes continual learning capability. Simulation experiments showed that the proposed algorithm has the superior classification performance compared with state-of-the-art clustering-based classification algorithms capable of continual learning.