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In recent years many works have shown that unsupervised Machine Learning (ML)can help detect unusual objects and uncover trends in large astronomicaldatasets, but a few challenges remain. We show here, for example, thatdifferent methods, or even small variations of the same method, can producesignificantly different outcomes. While intuitively somewhat surprising, thiscan naturally occur when applying unsupervised ML to highly dimensional data,where there can be many reasonable yet different answers to the same question.In such a case the outcome of any single unsupervised ML method should beconsidered a sample from a conceivably wide range of possibilities. Wetherefore suggest an approach that eschews finding an optimal outcome, insteadfacilitating the production and examination of many valid ones. This can beachieved by incorporating unsupervised ML into data visualisation portals. Wepresent here such a portal that we are developing, applied to the sample ofSDSS spectra of galaxies. The main feature of the portal is interactive 2D mapsof the data. Different maps are constructed by applying dimensionalityreduction to different subspaces of the data, so that each map containsdifferent information that in turn gives a different perspective on the data.The interactive maps are intuitive to use, and we demonstrate how peculiarobjects and trends can be detected by means of a few button clicks. We believethat including tools in this spirit in next generation astronomical surveyswill be important for making unexpected discoveries, either by professionalastronomers or by citizen scientists, and will generally enable the benefits ofvisual inspection even when dealing with very complex and extensive datasets.Our portal is available online at galaxyportal.space.

Effectively using unsupervised machine learning in next generation astronomical surveys