Expert on data mining to speak at KU


By Michelle Ward

Finding patterns and relevant information within data can help prevent diseases, combat terrorism and pinpoint business trends. A leader in data mining research will visit the University of Kansas to discuss the analysis of large data sets.

Jiawei Han, a professor of computer science at the University of Illinois, has begun analyzing immense interconnected networks--from the human genome to credit card transactions--to better locate treasures hidden within data networks. He will present "Mining Heterogeneous Information Networks by Exploring the Power of Links" at 3 p.m. Friday, April 16, at the Apollo Room in Nichols Hall. The talk is sponsored by the Information and Telecommunication Technology Center.

According to the Economist, the information management industry is growing 10 percent a year, roughly twice as fast as the software business as a whole. The industry is estimated to be worth more than $100 billion. Data mining was initially used to detect fraud and waste. Today, retailers, health care companies and financial institutions analyze large data sets to find ways to reduce costs and increase sales. Credit cards, frequent shopper cards and other profiling information provide mountains of data on shopping patterns and trends.

By examining data from the Web and other sources, counter terrorism officials help identify money transfers and communications among suspected terrorists.

"Professor Han is a leader and a pioneer researcher in data mining and database fields,"said Xue-wen Chen, director of ITTCs Bioinformatics and Computational Life-Sciences Laboratory, who was instrumental in bringing Han to KU. "His work has been instrumental in many areas, including knowledge discovery in databases, object-oriented databases, spatial databases, text and Web mining, bio-data mining and social networks."

Han will discuss link-based information integration, truth validation, clustering and integrated clustering and ranking during his talk. He will introduce recent research and explain ongoing studies that include object distinction analysis, veracity analysis and RankClus: integrated clustering and ranking.

Han has more than 400 journal and conference publications in data mining, data warehousing, database systems and information network analysis. He is the founding editor-in-chief of Association for Computing Machinery Transactions on Knowledge Discovery from Data. Han is a fellow of the Association for Computing Machinery and the Institute of Electric and Electronic Engineers.

For more information, contact ITTC.

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