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We propose a new method for clustering based on local minimization of the gamma-divergence, which we call spontaneous clustering. The greatest advantage of the proposed method is that it automatically detects the number of clusters that adequately reflect the data structure. In contrast, existing methods, such as K-means, fuzzy c-means, or model-based(More)
Contamination of scattered observations, which are either featureless or unlike the other observations, frequently degrades the performance of standard methods such as K-means and model-based clustering. In this letter, we propose a robust clustering method in the presence of scattered observations called Gamma-clust. Gamma-clust is based on a robust(More)
We have addressed a statistical estimation problem on Gaussian copula model, where a working model is Gaussian copula but an underlying distribution may be largely different from Gaussian copula. Usual estimators, like maximum likelihood estimators (MLEs), do not work well when such the large model misspecification occurs. We have proposed to apply a gamma(More)
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