Exact Recovery of Clusters in Finite Metric Spaces Using Oracle Queries

• URL: http://arxiv.org/abs/2102.00504v1
• Date: Sun, 31 Jan 2021 18:00:29 GMT
• Title: Exact Recovery of Clusters in Finite Metric Spaces Using Oracle Queries
• Authors: Marco Bressan, Nicol\`o Cesa-Bianchi, Silvio Lattanzi, Andrea Paudice
• Abstract summary: We investigate the problem of exact cluster dependence using oracle queries. We show that if we are allowed to make $(k2 + k )$, an additional queries can recover different different and arbitrary arbitrary arbitrary arbitrary arbitrary arbitrary arbitrary arbitrary arbitrary arbitrary arbitrary arbitrary arbitrary arbitrary arbitrary arbitrary arbitrary arbitrary arbitrary arbitrary arbitrary arbitrary arbitrary arbitrary arbitrary arbitrary arbitrary arbitrary arbitrary arbitrary arbitrary arbitrary arbitrary arbitrary arbitrary arbitrary arbitrary arbitrary arbitrary arbitrary arbitrary arbitrary arbitrary arbitrary arbitrary arbitrary arbitrary arbitrary arbitrary arbitrary arbitrary arbitrary arbitrary arbitrary arbitrary arbitrary arbitrary arbitrary arbitrary arbitrary arbitrary arbitrary arbitrary arbitrary arbitrary arbitrary arbitrary arbitrary arbitrary arbitrary arbitrary arbitrary arbitrary arbitrary arbitrary arbitrary arbitrary arbitrary arbitrary arbitrary arbitrary arbitrary arbitrary
• Score: 22.672233769934845
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We investigate the problem of exact cluster recovery using oracle queries. Previous results show that clusters in Euclidean spaces that are convex and separated with a margin can be reconstructed exactly using only $O(\log n)$ same-cluster queries, where $n$ is the number of input points. In this work, we study this problem in the more challenging non-convex setting. We introduce a structural characterization of clusters, called $(\beta,\gamma)$-convexity, that can be applied to any finite set of points equipped with a metric (or even a semimetric, as the triangle inequality is not needed). Using $(\beta,\gamma)$-convexity, we can translate natural density properties of clusters (which include, for instance, clusters that are strongly non-convex in $R^d$) into a graph-theoretic notion of convexity. By exploiting this convexity notion, we design a deterministic algorithm that recovers $(\beta,\gamma)$-convex clusters using $O(k^2 \log n + k^2 (\frac{6}{\beta\gamma})^{dens(X)})$ same-cluster queries, where $k$ is the number of clusters and $dens(X)$ is the density dimension of the semimetric. We show that an exponential dependence on the density dimension is necessary, and we also show that, if we are allowed to make $O(k^2 + k \log n)$ additional queries to a "cluster separation" oracle, then we can recover clusters that have different and arbitrary scales, even when the scale of each cluster is unknown.

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