Learning to Cluster via Same-Cluster Queries
This addresses a practical clustering challenge for data analysis by relaxing common assumptions, though it appears incremental in its algorithmic approach.
The paper tackles the problem of clustering data points using same-cluster queries without prior knowledge of the number of clusters or reliance on predefined objective functions like K-means, and demonstrates effectiveness through experiments on synthetic and real-world data.
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.