Data collected about a phenomenon often measures its magnitude and direction. The most common approach to clustering this data assumes that directional data can be modeled as Gaussian. However, directional data has special properties that conventional statistics cannot handle. To deal with them, other approaches like the von Mises distribution must be applied. In this paper we present a new model based on mixtures of Bayesian networks to simultaneously cluster both linear and directional data.
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