Constraint-Based Clustering Selection

TL;DR

This paper introduces a novel semi-supervised clustering selection method that uses user-provided constraints to choose the best clustering from various algorithms and parameters, often outperforming existing methods.

Contribution

It proposes a new approach to semi-supervised clustering by selecting among different algorithms using constraints, rather than modifying a single algorithm.

Findings

The method often outperforms existing semi-supervised clustering techniques.

Using constraints for algorithm selection improves clustering quality.

Empirical results demonstrate the effectiveness of the proposed approach.

Abstract

Semi-supervised clustering methods incorporate a limited amount of supervision into the clustering process. Typically, this supervision is provided by the user in the form of pairwise constraints. Existing methods use such constraints in one of the following ways: they adapt their clustering procedure, their similarity metric, or both. All of these approaches operate within the scope of individual clustering algorithms. In contrast, we propose to use constraints to choose between clusterings generated by very different unsupervised clustering algorithms, run with different parameter settings. We empirically show that this simple approach often outperforms existing semi-supervised clustering methods.