The paper shows a clustering application of a density estimation method that utilizes the Gaussian mixture model and the regularization theory. We define a "closeness measure" as a clustering criterion to see how close two Gaussian components are. The closeness measure is defined as the ratio of log likelihood between two Gaussian components. According to simulations using artificial data, the clustering algorithm turned out to be very powerful in that it can correctly determine clusters in complex situations, and very flexible in that it can produce different sizes of clusters based on different threshold values.