A Practical Algorithm for Distributed Clustering and Outlier Detection

TL;DR

This paper introduces a practical, efficient distributed clustering algorithm that effectively handles outliers, providing theoretical guarantees and outperforming baseline methods in experiments.

Contribution

It presents the first practical distributed clustering algorithm with outlier detection that offers theoretical guarantees and demonstrates superior performance.

Findings

Efficient algorithm with low communication overhead.

Strong approximation guarantees for clustering quality.

Outperforms baseline algorithms in real and synthetic data experiments.

Abstract

We study the classic $k$-means/median clustering, which are fundamental problems in unsupervised learning, in the setting where data are partitioned across multiple sites, and where we are allowed to discard a small portion of the data by labeling them as outliers. We propose a simple approach based on constructing small summary for the original dataset. The proposed method is time and communication efficient, has good approximation guarantees, and can identify the global outliers effectively. To the best of our knowledge, this is the first practical algorithm with theoretical guarantees for distributed clustering with outliers. Our experiments on both real and synthetic data have demonstrated the clear superiority of our algorithm against all the baseline algorithms in almost all metrics.