  We introduce a fast and explainable clustering method called CLASSIX. It
consists of two phases, namely a greedy aggregation phase of the sorted data
into groups of nearby data points, followed by the merging of groups into
clusters. The algorithm is controlled by two scalar parameters, namely a
distance parameter for the aggregation and another parameter controlling the
minimal cluster size. Extensive experiments are conducted to give a
comprehensive evaluation of the clustering performance on synthetic and
real-world datasets, with various cluster shapes and low to high feature
dimensionality. Our experiments demonstrate that CLASSIX competes with
state-of-the-art clustering algorithms. The algorithm has linear space
complexity and achieves near linear time complexity on a wide range of
problems. Its inherent simplicity allows for the generation of intuitive
explanations of the computed clusters.
