@article {13457, title = {Open problems in relational data clustering}, journal = {Proceedings of the ICML Workshop on Open Problems in Stastistical Relational Learning}, year = {2006}, month = {2006///}, abstract = {Data clustering is the task of detecting pat-terns in a set of data. Most algorithms take non-relational data as input and are sometimes unable to find significant patterns. Many data sets can include relational infor- mation, as well as independent object at- tributes. We believe that clustering with re- lational data will help find significant pat- terns where non-relational algorithms fail. This paper discusses two open problems in relational data clustering: clustering hetero- geneous data, and relation selection or ex- traction. Potential methods for addressing the problems are presented. }, author = {Anthony,A. and desJardins, Marie} }