Relational data community generation is concerned with learning community structures from relational data which involve rich collections of objects linked together in complex relational networks. Relational data community generation is a recently emerged hot topic in machine learning and data mining research, and solutions developed in the research hold substantial impacts in many important applications. A few examples of the important applications include Web community mining, social network mining, and law enforcement activities. It is worth noting that while there are so many important applications on relational data community generation, the research on this topic also holds direct impacts in promoting and advancing the literature and understandings of machine learning and data mining in other topics such as graph partitioning, spectral clustering, and statistical learning.
This tutorial aims to review and link the existing related work on this topic and to present a unified theory and the related frameworks on relational data community generation as well as its applications in different areas based on the recent developments of the research on this topic. This tutorial will provide an introduction to the theory, practice, and open problems in relational data community generation. Among the topics that will be covered are formulation and representation of relational data; the graph approximation model; the matrix approximation model; spectral approaches; semi-definite programming approaches; probabilistic models; a unified theory; and applications. Attendees will get a broad overview of both the theory and practice of relational data community generation, with numerous examples of how both impact real-world applications.
This tutorial should be of interest to a wide audience because relational data community generation are applicable in a wide range of problem domains. In particular, we expect the audience from the Web search applications and social network mining applications including financial institutions and law enforcement agencies as well as the related government agencies.
Bo Long is a Ph.D. candidate at Computer Science Department, SUNY at Binghamton. He worked as an intern at Yahoo! research lab, Google research lab, and IBM Watson Research Center. His research interests lie in data mining and machine learning with applications to natural language processing, Web mining, and bioinformatics, specifically in the development and analysis of algorithms for unsupervised learning on multi-type relational data. He has published papers in KDD, ICML and ICDM conferences. He is also serving as a PC member for KDD 2007.
Zhongfei (Mark) Zhang is currently an associate professor of Computer Science at the Computer Science Department at SUNY Binghamton. He received a B.S. in Electronics Engineering (with Honors), an M.S. in Information Sciences, both from Zhejiang University, China, and a PhD in Computer Science from the University of Massachusetts at Amherst. He was on the faculty of Computer Science and Engineering Department, and a research scientist at the Center of Excellence for Document Analysis and Recognition, both at SUNY Buffalo. His research interests include Multimedia Information Indexing and Retrieval, Data Mining and Knowledge Discovery, Computer Vision and Image Understanding, Pattern Recognition, as well as Bioinformatics. His research is sponsored by NSF, AFOSR, AFRL, and NYS, as well as private industries including Microsoft and Kodak. He has served as reviewers/PC members for many conferences and journals, as grant review panelists for governmental and private funding agencies. He has also served as technical consultants for a number of industrial and governmental organizations. He is a recipient of US National Academies/National Research Council Visiting Fellow, Air Force Research Laboratory Faculty Visiting Fellow, Microsoft Research Visiting Researcher, Western New York 2004 Inventor of the Year Individual Category 2nd Place, SUNY Chancellor's Promising Inventor Award, and JSPS International Collaboration Award.
Philip S. Yu received the B.S. Degree in E.E. from National Taiwan University, the M.S. and Ph.D. degrees in E.E. from Stanford University, and the M.B.A. degree from New York University. He is with the IBM Thomas J. Watson Research Center and currently manager of the Software Tools and Techniques group. His research interests include data mining, Internet applications and technologies, database systems, multimedia systems, parallel and distributed processing, and performance modeling. Dr. Yu has published more than 460 papers in refereed journals and conferences. He holds or has applied for more than 280 US patents.
Dr. Yu is a Fellow of the ACM and a Fellow of the IEEE. He is associate editors of ACM Transactions on the Internet Technology and ACM Transactions on Knowledge Discovery in Data. He is a member of the IEEE Data Engineering steering committee and is also on the steering committee of IEEE Conference on Data Mining. He was the Editor-in-Chief of IEEE Transactions on Knowledge and Data Engineering (2001-2004), an editor, advisory board member and also a guest co-editor of the special issue on mining of databases. He had also served as an associate editor of Knowledge and Information Systems. In addition to serving as program committee member on various conferences, he will be serving as the general chair of 2006 ACM Conference on Information and Knowledge Management and the program chair of the 2006 joint conferences of the 8th IEEE Conference on E-Commerce Technology (CEC' 06) and the 3rd IEEE Conference on Enterprise Computing, E-Commerce and E-Services (EEE' 06). He was the program chair or co-chairs of the 11th IEEE Intl. Conference on Data Engineering, the 6th Pacific Area Conference on Knowledge Discovery and Data Mining, the 9th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, the 2nd IEEE Intl. Workshop on Research Issues on Data Engineering: Transaction and Query Processing, the PAKDD Workshop on Knowledge Discovery from Advanced Databases, and the 2nd IEEE Intl. Workshop on Advanced Issues of E-Commerce and Web-based Information Systems. He served as the general chair of the 14th IEEE Intl. Conference on Data Engineering and the general co-chair of the 2nd IEEE Intl. Conference on Data Mining. He has received several IBM honors including 2 IBM Outstanding Innovation Awards, an Outstanding Technical Achievement Award, 2 Research Division Awards and the 88th plateau of Invention Achievement Awards. He received a Research Contributions Award from IEEE Intl. Conference on Data Mining in 2003 and also an IEEE Region 1 Award for "promoting and perpetuating numerous new electrical engineering concepts" in 1999. Dr. Yu is an IBM Master Inventor.