Abstract The k -Means algorithm is one of the most popular choices for clustering data but well-known to be sensitive initialization process. There a substantial number methods that aim at finding optimal initial seeds -Means, though none them universally valid. This paper presents an extension longitudinal such methods, BRIk algorithm, relies on set centroids derived from bootstrap replicates ...

We establish a functional central limit theorem for the empirical Ripley’s K-function of Gibbs point processes and with fast decay correlations. Our greatly extend past results that were restricted to Poisson case allow determine asymptotic behaviour statistics based on which may be used, example, develop goodness-of-fit tests. illustrate this in simulation study.