Initialization of Recursive Mixture-based Clustering with Uniform Components

The paper deals with a task of initialization of the recursive mixture estimation for the case of uniform components. This task is significant as a part of mixture-based clustering, where data clusters are described by the uniform distributions. The issue is extensively explored for normal components. However, sometimes the assumption of normality is not suitable or limits potential application areas (e.g., in the case of data with fixed bounds). The use of uniform components can be beneficial for these cases. Initialization is always a critical task of the mixture estimation. Within the considered recursive estimation algorithm the key point of its initialization is a choice of initial statistics of components. The paper explores several initialization approaches and compares results of clustering with a theoretical counterpart. Experiments with real data are demonstrated.