AI RESEARCH
Near-Optimal Clustering in Mixture of Markov Chains
arXiv CS.LG
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ArXi:2506.01324v3 Announce Type: replace-cross We study the problem of clustering $T$ trajectories of length $H$, each generated by one of K unknown ergodic Marko chains over a finite state space of size $S$. We derive an instance-dependent, high-probability lower bound on the clustering error rate, governed by the stationary-weighted KL divergence between transition kernels.