The well-recognized explosion of data being generated in many scientific disciplines and on the Web necessitates the development of effective scalable techniques to mine this data for knowledge in a (semi)-automated way. There has been a trade-off between the scalability of methods and the quality of knowledge extracted. This project aims to leverage the most robust machine learning algorithms (e.g. support vector machine kernel methods) with scalable clustering algorithms to permit the exploration of enormous data sets, to derive estimates of the expected performance of such methods, and to extend such methods so that they can learn new unanticipated concepts buried within the data. This research will have broader impact by making possible the use of effective, established, machine learning techniques on the large aggregations of data being generated in biology and other scientific disciplines, thus, potentially leading to the discovery of new knowledge hidden within the data.
