Abstract We consider the problem of fast time-series data clustering. Building on previous work modeling, correlation-based Hamiltonian spin variables we present an updated non-expensive agglomerative likelihood clustering algorithm (ALC). The method replaces optimized genetic based approach (f-SPC) with recursive merging framework inspired by in econophysics and community detection. is tested ...