Agglomerative Fast Super-Paramagnetic Clustering

Concretely, that the proposed algorithm does in fact recover the correct super-paramagnetic cluster configurations that are near the entropy maxima. Previous cases studies include data clustering of stocks [15] and gene data in [4], temporal states of financial markets [8], and state-detection for adaptive machine learning in trading [5]. There is an endless variety of potential use-cases for this type of fast big-data clustering technology. Building on prior work we propose and demonstrate an alternative to fast Super-Paramagnetic Clustering (f-SPC) [15] using a modern and streamlined implementation of the "Merging Algorithm" first suggested by Gi-ada [4], one that can recover the same cluster configurations for a variety of test-cases, but with significantly reduced compute times. We again use the Noh Ansatz [11] and the Maximum Likelihood Estimation approach introduced by Giada and Marsili [4]. We call the new algorithm Agglomerative Super-Paramagnetic Clustering (ASPC) and it has the benefit of being less computationally expensive than the PGAs implemented in [5, 6, 15].