  We present a novel method for identifying transients suitable for both strong
signal-dominated and background-dominated objects. By employing the
unsupervised machine learning algorithm known as Expectation Maximization, we
achieve computing time reductions of over $10^4$ on a single CPU compared to
conventional brute-force methods. Furthermore, this approach can be readily
extended to analyze multiple flares. We illustrate the algorithm's application
by fitting the IceCube neutrino flare of TXS 0506+056.
