Failures of Internet services and enterprise systems lead to user dissatisfaction and considerable loss of revenue. Since manual diagnosis is often laborious and slow, there is considerable interest in tools that can diagnose the cause of failures quickly and automatically from system-monitoring data. This paper identifies two key data-mining problems arising in a platform for automated diagnosis called Fa. Fa uses monitoring data to construct a database of failure signatures against which data from undiagnosed failures can be matched. Two novel challenges we address are to make signatures robust to the noisy monitoring data in production systems, and to generate reliable confidence estimates for matches. Fa uses a new technique called anomaly- based clustering when the signature database has no high- confidence match for an undiagnosed failure. This technique clusters monitoring data based on how it differs from the failure data, and pinpoints attributes linked to the failure. We show the effectiveness of Fa through a comprehensive experimental evaluation based on failures from a production setting, a variety of failures injected in a testbed, and synthetic data.