An Efficient Clustering Algorithm Using Stochastic Association Model and Its Implementation Using Nanostructures

Part of Advances in Neural Information Processing Systems 14 (NIPS 2001)

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Takashi Morie, Tomohiro Matsuura, Makoto Nagata, Atsushi Iwata


This paper describes a clustering algorithm for vector quantizers using a “stochastic association model”. It offers a new simple and powerful soft- max adaptation rule. The adaptation process is the same as the on-line K-means clustering method except for adding random fluctuation in the distortion error evaluation process. Simulation results demonstrate that the new algorithm can achieve efficient adaptation as high as the “neural gas” algorithm, which is reported as one of the most efficient clustering methods. It is a key to add uncorrelated random fluctuation in the simi- larity evaluation process for each reference vector. For hardware imple- mentation of this process, we propose a nanostructure, whose operation is described by a single-electron circuit. It positively uses fluctuation in quantum mechanical tunneling processes.