Self-regulated incremental clustering with focused preferences - WANG, Di (Institutional Knowledge at Singapore Management University, 2016)
Due to their online learning nature, incremental clustering techniques can handle a continuous stream of data. In particular, various incremental clustering techniques based on Adaptive Resonance Theory (ART) have been shown to have low computational complexity in adaptive learning and are less sensitive to noisy information. However, parameter regularization in existing ART clustering techniques is applied either on different features or on different clusters exclusively. In this paper, we introduce Interest-Focused Clustering based on Adaptive Resonance Theory (IFC-ART), which self-regulates the vigilance parameter associated with each feature and each cluster. As such, we can incorporate the domain knowledge of the data set into IFC-ART to focus on certain preferences during the self-regulated clustering process. For performance evaluation, we use a real-world data set, named American Time Use Survey (ATUS), which records nearly 160,000 telephone interviews conducted with U.S. residents from 2003 to 2014. Specifically, we conduct case studies to explore three types of interesting relationship, focusing on the wage, age, and provision of elderly care, respectively. Experimental results show that the performance of IFC-ART is highly competitive and stable when compared with two well-established clustering techniques and three ART models. In addition, we highlight the important and unexpected findings observed from the clusters discovered.