Professor Han has done groundbreaking work in the area of data mining. The rise of data mining reflects the imminent needs of today's computerized, data-intensive society. Data mining is an exciting scientific discipline since it requires us to integrate and advance the knowledge produced in multiple disciplines, including database systems, statistics, machine learning, algorithms, information networks, information retrieval, bioinformatics, Web technology, and high performance computing.
Data mining has been used extensively to identify anomalous conditions that may occur in the event of bugs or intrusions, and Professor Han has been working on trust and security in systems by pushing the boundaries of data mining techniques. His current projects address the issue of truth finding and outlier analysis as well as information security in information networks.
In particular, he has been working on the EventCube project on summarizing online text documents, such as news, tweets and other social media, finding general summaries as well as identifying outliers.
Currently, huge amounts of data are in the form of unstructured text data, Professor Han's group has been working on a data-intensive approach to conducting phrase mining and entity typing in order to turn unstructured text data into structured information networks, on top of which many information trust analysis methods can be developed and applied.