Fish 3D Clustering App (F3DCA): A Non-Supervised Clustering Method with a Machine Learning Autoencoder for Analyzing 3D Trajectories of Three Fish Species.

Fish behaviors are widely studied in various research fields due to their relevance to many aspects, including neurodegenerative processes, since they can impair motor control, complex behavior, habituation, and response to environmental stimuli. 3D locomotion trajectory analysis is one of the assays to analyze complex behaviors in fish. However, while existing 3D tracking software can capture the 3D trajectories of fish, they still rely heavily on manual or semi-automated analysis. Moreover, traditional analysis methods are inadequate for handling the high complexity of raw 3D data (X, Y, Z coordinates), and manual/semi-automated procedures introduce subjective human bias and are highly time-consuming, highlighting the need for a fully automated, unsupervised machine learning method. Here, we aimed to develop an unsupervised clustering method with machine learning to automatically analyze fish behaviors by using fish 3D coordinates. To evaluate the performance of this method, five different datasets, including (1) interspecies fish that consisted of three fish species, which were