Fast and explainable clustering based on sorting

Xinye Chen; Stefan Güttel

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arXiv · 2202.01456

Fast and explainable clustering based on sorting

Xinye Chen·Stefan Güttel

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Abstract

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.

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Xinye Chen, Stefan Güttel. 2022-02-03. Fast and explainable clustering based on sorting. https://arxiv.org/abs/2202.01456

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