Abstract
For successful clustering, an algorithm needs to find the boundaries between clusters. While this is comparatively easy if the clusters are compact and non-overlapping and thus the boundaries clearly defined, features where the clusters blend into each other hinder clustering methods to correctly estimate these boundaries. Therefore, we aim to extract features showing clear cluster boundaries and thus enhance the cluster structure in the data. Our novel technique creates a condensed version of the data set containing the structure important for clustering, but without the noise-information. We demonstrate that this transformation of the data set is much easier to cluster for k-means, but also various other algorithms. Furthermore, we introduce a deterministic initialisation strategy for k-means based on these structure-rich features.
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Notes
- 1.
We follow the argument given in [16] in regard to the explicit form of the derivative.
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Schelling, B., Bauer, L.G.M., Behzadi, S., Plant, C. (2021). Utilizing Structure-Rich Features to Improve Clustering. In: Hutter, F., Kersting, K., Lijffijt, J., Valera, I. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2020. Lecture Notes in Computer Science(), vol 12457. Springer, Cham. https://doi.org/10.1007/978-3-030-67658-2_6
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DOI: https://doi.org/10.1007/978-3-030-67658-2_6
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