Abstract
Three models for linear regression clustering are given, and corresponding methods for classification and parameter estimation are developed and discussed: The mixture model with fixed regressors (ML-estimation), the fixed partition model with fixed regressors (ML-estimation), and the mixture model with random regressors (Fixed Point Clustering). The number of clusters is treated as unknown. The approaches are compared via an application to Fisher’s Iris data. By the way, a broadly ignored feature of these data is discovered.
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© 1999 Springer-Verlag Berlin · Heidelberg
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Hennig, C. (1999). Models and Methods for Clusterwise Linear Regression. In: Gaul, W., Locarek-Junge, H. (eds) Classification in the Information Age. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-60187-3_17
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DOI: https://doi.org/10.1007/978-3-642-60187-3_17
Publisher Name: Springer, Berlin, Heidelberg
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