The goal of this research is to reduce the knowledge engineering required to apply clustering to real-world problems by changing how users interact with clustering systems. Instead of having users manually engineer distance metrics and modify clustering algorithms, meta clustering helps users find good clusterings by generating many different clusterings of the data, and presenting these to users organized as a meta clustering. A meta clustering is a clustering of clusterings that makes it easier for users to navigate the clustering space to find clusterings useful to them. An important goal of the project is to work with existing clustering methods instead of replacing them with new methods. By shifting some of the burden from users to the computer, the project will have significant impact on the practice, research, and education in clustering. This will make clustering easier, particularly for users not adept at defining distance metrics, help experienced users arrive at good clusterings faster, and will advance clustering research and education by providing a framework for comparing and evaluating clustering methods. In addition to producing fundamental research results, the research project will be used to train Ph.D. students, will involve both undergraduate and exceptional high school students in research, will help in creating a new course on unsupervised learning, will yield meta clustering software available through the web that is compatible with many existing clustering algorithms, and will form the basis of a novel workshop and web-based comparison of clustering methods run by different clustering researchers. The project Web site www.cs.cornell.edu/~caruana will be used for timely results dissemination.