Adaptive clustering using modular learning architecture
We present a clustering method for data that are continuously distributed within each class and where there is no overlap between the classes. With the method the data are clustered by on-line processing, starting at a point where the number of classes is unknown. The proposed method uses ideas derived from statistical mechanics and applies them to a modular learning architecture: learning progresses by regarding the data as particles and regarding each module as a space in which the particles are distributed. The learning method estimates the energy state that matches the particle distribution. As a result of learning, continuously distributed data are clustered in the same module. At the same time, certain modules appear that contain no data. Consequently, the number of classes is estimated by defining each module containing data as a class. This paper describes the proposed method in detail and shows the results of clustering experiments undertaken using two-dimensional artificial data and facial images. © 2003 Wiley Periodicals, Inc. Syst Comp Jpn, 34(2): 70–80, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/scj.1191