Imagine a machine learning agent deployed at each station in a sensor network, so that it can analyze incoming data and determine when something interesting happens. Traditionally, this analysis would be done independently at each station. But what if each agent could talk to its neighbors and find out what they're seeing? We’ve developed a learning system that enables collaboration so that the agents can autonomously (without human input) improve their performance. Each agent can ask its neighbors for their opinions, then use them to refine its own results. When each agent is given the task of clustering the observed data, the opinions are expressed in the form of pairwise clustering constraints. We evaluated several heuristics for selecting which items an agent should query and found that the best strategy was to select one item close to its assigned cluster and one item at the boundary between two clusters. We applied this technique to seismic and infrasonic data collected by the Mount Erebus Volcano Observatory, in which the goal was to separate eruptions from non-eruptions. Collaborative clustering achieved a 150% improvement over regular, non-collaborative clustering. This is joint work with Jillian Green (California State Univ., Los Angeles), Rich Aster and Hunter York (New Mexico Institute of Mining and Technology), Terran Lane (Univ. of NM), and Umaa Rebbapragada (JPL), funded by the NSF.