Abstract
Finite Mixture of Regression (FMR) models account for the target variable and cluster the data points based on the relationship between inputs and target variables. However, extant FMR models mostly rely on linear regression models. We propose a competitive learning algorithm to perform FMR modeling, which allows nonlinear models such a Multi-Layer Perceptrons (MLPs) to carry out the regression. We demonstrate the proposed method using a real-world case study that aims to derive tailored recommendations for franchises to improve their profitability and sales effectiveness.
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