  We consider the problem of fast time-series data clustering. Building on
previous work modeling the correlation-based Hamiltonian of spin variables we
present an updated fast non-expensive Agglomerative Likelihood Clustering
algorithm (ALC). The method replaces the optimized genetic algorithm based
approach (f-SPC) with an agglomerative recursive merging framework inspired by
previous work in Econophysics and Community Detection. The method is tested on
noisy synthetic correlated time-series data-sets with built-in cluster
structure to demonstrate that the algorithm produces meaningful non-trivial
results. We apply it to time-series data-sets as large as 20,000 assets and we
argue that ALC can reduce compute time costs and resource usage cost for large
scale clustering for time-series applications while being serialized, and hence
has no obvious parallelization requirement. The algorithm can be an effective
choice for state-detection for online learning in a fast non-linear data
environment because the algorithm requires no prior information about the
number of clusters.
