k-Center Clustering with Outliers in Sliding Windows

TL;DR

This paper introduces efficient streaming algorithms for k-center clustering with outliers in sliding windows, achieving constant approximation with minimal memory and providing practical estimates of data spread.

Contribution

It presents the first algorithms for k-center clustering with outliers in sliding windows that are both memory-efficient and achieve a constant approximation ratio.

Findings

Algorithms achieve O(1) approximation ratio.

Memory usage is linear in k+z and logarithmic in window size.

Experimental results confirm practical viability.

Abstract

Metric $k$-center clustering is a fundamental unsupervised learning primitive. Although widely used, this primitive is heavily affected by noise in the data, so that a more sensible variant seeks for the best solution that disregards a given number $z$ of points of the dataset, called outliers. We provide efficient algorithms for this important variant in the streaming model under the sliding window setting, where, at each time step, the dataset to be clustered is the window $W$ of the most recent data items. Our algorithms achieve $O(1)$ approximation and, remarkably, require a working memory linear in $k+z$ and only logarithmic in $|W|$. As a by-product, we show how to estimate the effective diameter of the window $W$, which is a measure of the spread of the window points, disregarding a given fraction of noisy distances. We also provide experimental evidence of the practical viability of our theoretical results.