【主題】頻繁模式挖掘的最新進展

【主講人】美國伊利諾大學香槟分校計算機科學系教授韓家炜

【時間】2007-5-28 10:30-11:30

【地點】偉倫樓405

【語言】英語

【主辦】管理科學與工程系

【目标聽衆】

【簡介】

ABSTRACT

Recent research progress in frequent pattern mining bring new promise indata mining applications in two aspects: (1) feature extraction for effective classification and (2) mining colossal patterns. My talk will cover the progress on these two themes based on our two recent research papers at ICDE'07. In the mean time, I am going to discuss a few promising research directions and predict their impacts on data mining.

Short bio:

Jiawei Han, Professor, Department of Computer Science, University ofIllinois at Urbana-Champaign. He has been working on research into data mining, data warehousing, database systems, data mining from spatiotemporal data, multimedia data, stream and RFID data, social network data, and biological data, with over 300 journal and conference publications. He has chaired or served in over 100 program committees of international conferences and workshops, including PC co-chair of 2005 (IEEE) International Conference on Data Mining (ICDM), Americas Coordinator of 2006 International Conference on Very Large Data Bases (VLDB). He is also serving as the founding Editor-In-Chief of ACM Transactions on Knowledge Discovery from Data. He is an ACM Fellow and has received 2004 ACM SIGKDD Innovations Award and 2005 IEEE Computer Society Technical Achievement Award. His book "Data Mining: Concepts and Techniques" (2nd ed., Morgan Kaufmann, 2006) has been popularly used as a textbook worldwide.