## 1 Introduction  
With the recent development of IoT technology, a wide variety of big data from different domains has become easily available. Many studies have proposed machine learning algorithms for classification and clustering to learn useful features/information from the data. One of the major challenges in the field of machine learning is to achieve continual learning like the human brain using computational algorithms. In general, it is difficult for many computational algorithms to avoid catastrophic forgetting, i.e., previously learned features/information is collapsed due to learning new ones [1], which makes it difficult for the algorithms to realize efficient learning from big data with increasing amounts and types of data. Recently, therefore, computational algorithms capable of continual learning have attracted much attention as a promising approach for continually and efficiently learning useful features/information from big data [2]. Continual learning is categorized into three scenarios: domain incremental learning, task incremental learning, and class incremental learning [34]. This paper focuses on class incremental learning, which includes the common real-world problem of incrementally learning new classes of information. In the case of layer-wise neural networks capable of continual learning, their memory capacity is often limited because most of the network architecture is fixed [5]. One promising approach to overcome the limitation of memory capacity is to apply a growing self-organizing clustering algorithm as a classifier. The Self-Organizing Incremental Neural Network (SOINN) [6] is a well-known growing self-organizing clustering algorithm which is inspired by Growing Neural Gas (GNG) [7]. Several conventional studies have shown that SOINN-based classifiers capable of continual learning have good classification performance [8; 9; 10]. However, the self-organizing process of SOINN-based algorithms is highly unstable due to the instability of GNG-based algorithms. This paper proposes a new classification algorithm capable of continual learning by utilizing a growing self-organizing clustering based on Adaptive Resonance Theory (ART) [11]. Among recent ART-based clustering algorithms, an algorithm that utilizes Correntropy-Induced Metric (CIM) [12] as a similarity measure shows faster and more stable self-organizing performance than GNG-based algorithms [13; 14; 15]. We apply CIM-based ART with Edge and Age (CAEA) [16] as a base clustering algorithm in the proposed algorithm. Moreover, we also propose two variants of the proposed algorithm by modifying a computation of the CIM to improve the classification performance. It is expected that the proposed algorithms can utilize large amounts of data more efficiently than conventional algorithms by continually generating classifiers with high classification performance. The contributions of this paper are summarized as follows:  
1. A new classification algorithm capable of continual learning, called CAEA Classifier (CAEAC), is proposed by applying an ART-based clustering algorithm. CAEAC generates a new clustering space to learn a new classifier each time a new class of data is given. In addition, thanks to the ART-based clustering algorithm, CAEAC realizes fast and stable computation while avoiding catastrophic forgetting in each clustering space. 2. Two variants of CAEAC, namely CAEAC-Individual (CAEAC-I) and CAEAC-Clustering (CAEAC-C) are introduced to improve the classification performance of CAEAC. These variants use a modified version of the CIM computation (see Section 3.4 in detail). 3. Empirical studies show that CAEAC and its variants have superior classification performance to state-of-the-art clustering-based classifiers. 4. The parameter sensitivity of CAEAC (and its variants) is analyzed in detail. The paper is organized as follows. Section 2 presents a review of the literature on growing self-organizing clustering algorithms and classification algorithms capable of continual learning.