Unsupervised Single-link Hierarchical Clustering
نویسنده
چکیده
There are many clustering techniques presented in the literature. The particularity of single-link clustering is that it rather discovers the clusters as chains. We aim to identify a method to apply the single link clustering technique so that: it discovers the first level clusters and the user doesn’t have to provide any sort of a parameter. We focuses on clusters that are well separated, and so, which have to maximize the intra-cluster similarity and minimize the inter-cluster similarity. We evaluate the method on a two dimensional space, that is planar points.
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