Clustering Geometrically-Modeled Points in the Aggregated Uncertainty Model

TL;DR

This paper introduces a generalized uncertain $k$-center clustering problem for geometric regions, proposes approximation algorithms, and demonstrates their practical effectiveness through implementation.

Contribution

It extends the region-based uncertainty model to allow multiple points per region and provides approximation algorithms for the generalized $k$-center problem.

Findings

Approximation algorithms achieve theoretical guarantees.

Algorithms perform well on real data sets.

The problem is NP-hard with a known lower bound.

Abstract

The $k$-center problem is to choose a subset of size $k$ from a set of $n$ points such that the maximum distance from each point to its nearest center is minimized. Let $Q=\{Q_1,\ldots,Q_n\}$ be a set of polygons or segments in the region-based uncertainty model, in which each $Q_i$ is an uncertain point, where the exact locations of the points in $Q_i$ are unknown. The geometric objects segments and polygons can be models of a point set. We define the uncertain version of the $k$-center problem as a generalization in which the objective is to find $k$ points from $Q$ to cover the remaining regions of $Q$ with minimum or maximum radius of the cluster to cover at least one or all exact instances of each $Q_i$, respectively. We modify the region-based model to allow multiple points to be chosen from a region and call the resulting model the aggregated uncertainty model. All these problems contain the point version as a special case, so they are all NP-hard with a lower bound 1.822. We give approximation algorithms for uncertain $k$-center of a set of segments and polygons. We also have implemented some of our algorithms on a data-set to show our theoretical performance guarantees can be achieved in practice.