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.

An increasing number of people reports sleeping problems. In the present paper we describe Somnia: a system designed to support remote couples to fall asleep faster and to enhance their sleep quality. Following a user-centered design process, Somnia was prototyped and evaluated. Qualitative feedback after a two-week user study showed that Somnia succeeded in providing a sense of connectedness between partners when sleeping remotely. This sense of connectedness might lead to a more pleasant sleeping experience. Based on our findings, we recommend designers of sleep related technologies to (a) incorporate the social aspects of sleep in their designs and (b) to focus on emotional arguments rather than rational arguments to influence sleeping habits.