Abstract
Clustering is a basic tool in unsupervised machine learning and data mining. Distance-based clustering algorithms rarely have the means to autonomously come up with the correct number of clusters from the data. A recent approach to identifying the natural clusters is to compare the point densities in different parts of the sample space.
In this paper we put forward an agglomerative clustering algorithm which accesses density information by constructing a Voronoi diagram for the input sample. The volumes of the point cells directly reflect the point density in the respective parts of the instance space. Scanning through the input points and their Voronoi cells once, we combine the densest parts of the instance space into clusters.
Our empirical experiments demonstrate the proposed algorithm is able to come up with a high-accuracy clustering for many different types of data. The Voronoi approach clearly outperforms k-means algorithm on data conforming to its underlying assumptions.
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Koivistoinen, H., Ruuska, M., Elomaa, T. (2006). A Voronoi Diagram Approach to Autonomous Clustering. In: Todorovski, L., Lavrač, N., Jantke, K.P. (eds) Discovery Science. DS 2006. Lecture Notes in Computer Science(), vol 4265. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893318_17
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DOI: https://doi.org/10.1007/11893318_17
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