The mixture of experts framework is widely utilized in statistics and ma- chine learning to address data heterogeneity in tasks such as regression, classification, and clustering. In clustering continuous data, the mixture of experts typically em- ploys experts that follow a Gaussian distribution. However, outliers can adversely affect clustering outcomes. To address this issue, various methods have been proposed in the literature. In this paper, we introduce a novel approach that models the ex- perts using the symmetric α-stable distribution. This flexible distribution effectively accommodates different types of outliers (especially extreme outliers) and skewness, while also encompassing Gaussian experts as a special case when α = 2. The maxi- mum likelihood estimates of the model parameters (excluding α) are obtained using an expectation-maximization approach, while α is estimated using Monte Carlo inte- gration and interpolation. The effectiveness of this approach is demonstrated through analyses of both real and simulated data.
