Title: Clustering Analysis on Locally Asymptotically Self-similar Processes with Known Number of Clusters,
Abstract: We study the problems of clustering locally asymptotically self-similar
stochastic processes, when the true number of clusters is priorly known. A new
covariance-based dissimilarity measure is introduced, from which the so-called
approximately asymptotically consistent clustering algorithms are obtained. In
a simulation study, clustering data sampled from multifractional Brownian
motions is performed to illustrate the approximated asymptotic consistency of
the proposed algorithms.