  Classification will be an important first step for upcoming surveys that will
detect billions of new sources such as LSST and Euclid, as well as DESI, 4MOST
and MOONS. The application of traditional methods of model fitting and
colour-colour selections will face significant computational constraints, while
machine-learning (ML) methods offer a viable approach to tackle datasets of
that volume. While supervised learning methods can perform very well for
classification tasks, the creation of representative and accurate training sets
is a resource and time consuming task. We present a viable alternative using an
unsupervised ML method to separate stars, galaxies and QSOs using photometric
data. The heart of our work uses HDBSCAN to find the star, galaxy and QSO
clusters in a multidimensional colour space. We optimized the hyperparameters
and input attributes of three separate HDBSCAN runs, each to select a
particular object class, and thus treat the output of each separate run as a
binary classifier. We subsequently consolidate the output to give our final
classifications, optimized on their F1 scores. We explore the use of Random
Forest and PCA as part of the pre-processing stage for feature selection and
dimensionality reduction. Using our dataset of 50,000 spectroscopically
labelled objects we obtain an F1 score of 98.9, 98.9 and 93.13 respectively for
star, galaxy and QSO selection using our unsupervised learning method. We find
that careful attribute selection is a vital part of accurate classification
with HDBSCAN. We applied our classification to a subset of the SDSS
spectroscopic catalogue and demonstrate the potential of our approach in
correcting misclassified spectra useful for DESI and 4MOST. Finally, we create
a multiwavelength catalogue of 2.7 million sources using the KiDS, VIKING and
ALLWISE surveys and publish corresponding classifications and photometric
redshifts.
