Abstract
Classification maximum likelihood (CML) procedure is a maximum likelihood mixture approach to clustering. In 1993, Yang first extended the CML to a so-called fuzzy CML (FCML), by combining fuzzy c-partitions with the CML function for a normal mixture model. However, normal distribution is not robust for outliers. In this paper we consider FCML with t-distributions and create a clustering algorithm, called FCMLT. Numerical examples and real data applications with comparisons are given to demonstrate the effectiveness and superiority of the proposed method.
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