Abstract
The amount of data increases steadily, and yet most clustering algorithms perform complex computations for every single data point. Furthermore, Euclidean distance which is used for most of the clustering algorithms is often not the best choice for datasets with arbitrarily shaped clusters or such with high dimensionality. Based on ABOD, we introduce ABC, the first angle-based clustering method. The algorithm first identifies a small part of the data as border points of clusters based on the angle between their neighbors. Those few border points can, with some adjustments, be clustered with well-known clustering algorithms like hierarchical clustering with single linkage or DBSCAN. Residual points can quickly and easily be assigned to the cluster of their nearest border point, so the overall runtime is heavily reduced while the results improve or remain similar.
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This work has been funded by the German Federal Ministry of Education and Research (BMBF) under Grant No. 01IS18036A. The authors of this work take full responsibilities for its content.
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Beer, A., Seeholzer, D., Schüler, NS., Seidl, T. (2020). Angle-Based Clustering. In: Satoh, S., et al. Similarity Search and Applications. SISAP 2020. Lecture Notes in Computer Science(), vol 12440. Springer, Cham. https://doi.org/10.1007/978-3-030-60936-8_24
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DOI: https://doi.org/10.1007/978-3-030-60936-8_24
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