Delayed Assignments in Online Non-Centroid Clustering with Stochastic Arrivals

TL;DR

This paper introduces a new framework for online non-centroid clustering with delayed decisions in stochastic settings, achieving constant competitive ratios by balancing delay and clustering costs.

Contribution

It proposes a novel stochastic arrival model for online clustering with delays and presents an algorithm with a constant competitive ratio, surpassing worst-case limitations.

Findings

Algorithm achieves constant competitive ratio in stochastic model.

Delaying assignments effectively balances clustering and delay costs.

Overcomes worst-case sublogarithmic bounds in online clustering.

Abstract

Clustering is a fundamental problem, aiming to partition a set of elements, like agents or data points, into clusters such that elements in the same cluster are closer to each other than to those in other clusters. In this paper, we present a new framework for studying online non-centroid clustering with delays, where elements, that arrive one at a time as points in a finite metric space, should be assigned to clusters, but assignments need not be immediate. Specifically, upon arrival, each point's location is revealed, and an online algorithm has to irrevocably assign it to an existing cluster or create a new one containing, at this moment, only this point. However, we allow decisions to be postponed at a delay cost, instead of following the more common assumption of immediate decisions upon arrival. This poses a critical challenge: the goal is to minimize both the total distance costs between points in each cluster and the overall delay costs incurred by postponing assignments. In the classic worst-case arrival model, where points arrive in an arbitrary order, no algorithm has a competitive ratio better than sublogarithmic in the number of points. To overcome this strong impossibility, we focus on a stochastic arrival model, where points' locations are drawn independently across time from an unknown and fixed probability distribution over the finite metric space. We offer hope for beyond worst-case adversaries: we devise an algorithm that is constant competitive in the sense that, as the number of points grows, the ratio between the expected overall costs of the output clustering and an optimal offline clustering is bounded by a constant.