›› 2009, Vol. 24 ›› Issue (6): 1018-1027.

Special Issue: Data Management and Data Mining

• Special Section on International Partnership Programs Supported by CAS • Previous Articles     Next Articles

Ubiquitous Mining with Interactive Data Mining Agents

Xin-Dong Wu1,2 (吴信东), Senior Member, IEEE, Xing-Quan Zhu3 (朱兴全), Member, ACM, IEEE, Qi-Jun Chen2 (陈琪君), and Fei-Yue Wang4 (王飞跃), Member, ACM, Fellow, IEEE   

  1. 1School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230009, China
    2Department of Computer Science, University of Vermont, Burlington, VT 05405, U.S.A.
    3Faculty of Engineering and Information Technology, University of Technology, Sydney, NSW 2007, Australia
    4Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
  • Received:2009-02-27 Revised:2009-07-09 Online:2009-11-05 Published:2009-11-05
  • About author:
    Xin-Dong Wu is a professor and the chair of the Computer Science Department at the University of Vermont, USA. He holds a Ph.D. degree in artificial intelligence from the University of Edinburgh, Britain. His research interests include data mining, knowledge-based systems, and Web information exploration. He has published over 180 refereed papers in these areas in various journals and conferences, including IEEE TKDE, TPAMI, ACM TOIS, DMKD, KAIS, IJCAI, AAAI, ICML, KDD, ICDM, and WWW, as well as 23 books and conference proceedings. His research has been supported by the U.S. National Science Foundation (NSF), the U.S. Department of Defense (DOD), the National Natural Science Foundation of China (NSFC), and the Chinese Academy of Sciences, as well as industrial companies including U.S. West Advanced Technologies and Empact Solutions. Prof. Wu is the founder and current steering committee chair of the IEEE International Conference on Data Mining (ICDM), the founder and current editor-in-chief of Knowledge And Information Systems (KAIS, by Springer), the founding chair (2002~2006) of the IEEE Computer Society Technical Committee on Intelligent Informatics (TCII), and a series editor of the Springer Book Series on Advanced Information and Knowledge Processing (AI&KP). He was the editor-in-chief of the IEEE Transactions on Knowledge and Data Engineering (TKDE, by the IEEE Computer Society) between January 1, 2005 and December 31, 2008, and served program committee chair for ICDM'03 (the 2003 IEEE International Conference on Data Mining) and as program committee co-chair for KDD-07 (the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining). Prof. Wu is the 2004 ACM SIGKDD Service Award winner, the 2006 IEEE ICDM Outstanding Service Award winner, and 2005 chair professor in the Changjiang (or Yangtze River) Scholars Program at the Hefei University of Technology appointed by the Ministry of Education of China. He has been an invited/keynote speaker at numerous international conferences including IEEE GrC 2009, IDEAL 2009, JCKBSE 2008, HAIS 2008, NSF-NGDM'07, PAKDD-07, IEEE EDOC'06, IEEE ICTAI'04, IEEE/WIC/ACM WI'04/IAT'04, SEKE 2002, and PADD-97.
    Xing-Quan Zhu received his Ph.D. degree in computer science from Fudan University, China, in 2001. He is currently an associate professor of the Faculty of Engineering and Information Technology, University of Technology, Sydney (UTS), Australia. Before joining UTS, he was a tenure track Assistant professor in the Department of Computer Science & Engineering, Florida Atlantic University, USA (2006~2009), a research assistant professor in the Department of Computer Science, University of Vermont, USA (2002~2006), and a postdoctoral associate in the Department of Computer Science, Purdue University, USA (2001~2002). Dr. Zhu's research mainly focuses on data mining, machine learning, and multimedia systems. Since 2000, he has published more than 90 refereed journal and conference proceedings papers in these areas. He is an associate editor of the IEEE Transactions on Knowledge and Data Engineering.
    Qi-Jun Chen received her Master's degree in computer science from the University of Vermont, USA. She is currently a database administrator of the West Virginia University. Her research interests include inductive learning from large databases.
  • Supported by:

    This research has been supported by the National Basic Research 973 Program of China under Grant No. 2009CB326203, the National Natural Science Foundation of China under Grant Nos. 60828005 and 60674109, and the Chinese Academy of Sciences under International Partnership Grant No. 2F05N01.

Due to the increasing availability and sophistication of data recording techniques, multiple information sources and distributed computing are becoming the important trends of modern information systems. Many applications such as security informatics and social computing require a ubiquitous data analysis platform so that decisions can be made rapidly under distributed and dynamic system environments. Although data mining has now been popularly used to achieve such goals, building a data mining system is, however, a nontrivial task, which may require a complete understanding on numerous data mining techniques as well as solid programming skills. Employing agent techniques for data analysis thus becomes increasingly important, especially for users not familiar with engineering and computational sciences, to implement an effective ubiquitous mining platform. Such data mining agents should, in practice, be intelligent, complete, and compact. In this paper, we present an interactive data mining agent --- OIDM (online interactive data mining), which provides three categories (classification, association analysis, and clustering) of data mining tools, and interacts with the user to facilitate the mining process. The interactive mining is accomplished through interviewing the user about the data mining task to gain efficient and intelligent data mining control. OIDM can help users find appropriate mining algorithms, refine and compare the mining process, and finally achieve the best mining results. Such interactive data mining agent techniques provide alternative solutions to rapidly deploy data mining techniques to broader areas of data intelligence and knowledge informatics.

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