  Clustering is inherently ill-posed: there often exist multiple valid
clusterings of a single dataset, and without any additional information a
clustering system has no way of knowing which clustering it should produce.
This motivates the use of constraints in clustering, as they allow users to
communicate their interests to the clustering system. Active constraint-based
clustering algorithms select the most useful constraints to query, aiming to
produce a good clustering using as few constraints as possible. We propose
COBRA, an active method that first over-clusters the data by running K-means
with a $K$ that is intended to be too large, and subsequently merges the
resulting small clusters into larger ones based on pairwise constraints. In its
merging step, COBRA is able to keep the number of pairwise queries low by
maximally exploiting constraint transitivity and entailment. We experimentally
show that COBRA outperforms the state of the art in terms of clustering quality
and runtime, without requiring the number of clusters in advance.
