Learning to Cluster via Same-Cluster Queries

TL;DR

This paper introduces algorithms for clustering data points using an oracle that answers same-cluster queries, without prior knowledge of the number of clusters or assuming a specific clustering objective, supported by theoretical guarantees and experiments.

Contribution

It presents new algorithms for clustering with same-cluster queries that do not require knowing the number of clusters or assuming a fixed objective, addressing practical challenges.

Findings

Algorithms with provable guarantees

Effective on synthetic data

Validated on real-world data

Abstract

We study the problem of learning to cluster data points using an oracle which can answer same-cluster queries. Different from previous approaches, we do not assume that the total number of clusters is known at the beginning and do not require that the true clusters are consistent with a predefined objective function such as the K-means. These relaxations are critical from the practical perspective and, meanwhile, make the problem more challenging. We propose two algorithms with provable theoretical guarantees and verify their effectiveness via an extensive set of experiments on both synthetic and real-world data.