UBC
UBC
UBC UBC
-
Computer Science
 
Dr. Carson K. Leung 
Associate Professor, Computer Science, U. Manitoba
     E2-445 EITC, U. Manitoba, Winnipeg, MB, Canada   R3T 2N2
Visiting Associate Professor, Computer Science, UBC 
     201-2366 Main Mall, UBC, Vancouver, BC, Canada   V6T 1Z4

E-mail: kleung [AT] cs . ubc . ca
Phone: +1 (604) 822-3061 (dept); 2-0826 (office); 7-3984 / 7-5909 (lab)
Fax: +1 (604) 822-5485
Office: ICICS/CS 339
Lab: ICICS/CS Addition X410 and X415 (Data Management and Mining Lab)
 

B.Sc. (Honours), The University of British Columbia; M.Sc., The University of British Columbia; Ph.D., The University of British Columbia.

Research Interests

  • Databases (including image databases)
  • Data mining and analysis
  • Data management
  • E-health
  • Visual analytics
Data mining refers to the search for previously unknown patterns and relationships that might be embedded in stored data. Most of the existing data mining algorithms treat the mining process as an impenetrable black-box, where users are not allowed to express their focus (user-specified constraints). As a result, these unconstrained mining algorithms can yield numerous patterns that do not make sense (e.g., "customers who buy diapers also buy beer") or that are not interesting to users. To this end, we are developing a human-centered exploratory mining algorithm that (i) enables human analysts/users to impose constraints to focus the search, and (ii) avoids irrelevant and time-consuming computation. Such an algorithm shows an excellent division of labor, where the computer carries out the mechanical aspect of the work (e.g., the counting and searching) and the human performs the intelligent aspect of the work (e.g., the abstract thinking and observation).

It is understood that data mining is supposed to be an iterative and exploratory process. Hence, we not only allow users to impose certain constraints on the mining process, but also allow users to change these constraints dynamically in the middle of the computation. Towards the development of a practical environment of this human-centered exploratory mining algorithm, we are developing techniques to support dynamic mining. To enhance the performance of our dynamic mining algorithm, we have proposed the following novel structures: (i) the segment support map to facilitate scalable mining, and (ii) the OSSM to optimize frequency counting.

With respect to my research interest on image databases, the motivation is as follows. As the number of on-line digital images has increased rapidly, the development of efficient and effective retrieval of images is necessary. Many existing image database systems support whole-image queries, which require users to specify the contents of the whole images to be retrieved. However, users may only remember or care about some, but not all, portions of the images (i.e., subimages) they have seen before. Techniques for handling subimage queries of arbitrary size are therefore in demand. Unfortunately, not many image database management systems can handle these subimage queries. Among the systems that can deal with subimage queries of arbitrary size, multiscale similarity matching is rarely used. To this end, we developed techniques based on multiscale similarity matching to handle subimage queries of arbitrary size, and applied the techniques in large image databases.

Selected Publications

  1. C.K.-S. Leung and C.L. Carmichael. FpVAT: A visual analytic tool for supporting frequent pattern mining. SIGKDD Explorations, 11(2), special issue on visual analytics and knowledge discovery, pages 39-48, December 2009.
  2. M.A.F. Mateo and C.K.-S. Leung. CHARIOT: A comprehensive data integration and quality assurance model for agro-meteorogical data. In C.-Y. Chan et al. (eds.), Data Quality and High-Dimensional Data Analysis, pages 21-41, February 2009.
  3. C.K.-S. Leung, M.A.F. Mateo, and D.A. Brajczuk. A tree-based approach for frequent pattern mining from uncertain data. In Proceedings of the PAKDD 2008 (LNAI 5012), pages 653-661. Osaka, Japan, May 2008.
  4. B. Hao, C.K.-S. Leung, S. Camorlinga, M.H. Reed, M.K. Bunge, J. Wrogemann, and R.J. Higgins. A computer-aided change detection system for paediatric acute intracranial haemorrhage. In Proceedings of the C3S2E 2008, pages 109-111, Montréal, QC, Canada, May 2008.
  5. C.K.-S. Leung, Q.I. Khan, Z. Li, and T. Hoque. CanTree: A canonical-order tree for incremental frequent-pattern mining. Knowledge and Information Systems (KAIS) 11(3), pages 287-311, April 2007.
  6. L.V.S. Lakshmanan, C.K.-S. Leung, and R.T. Ng. Efficient dynamic mining of constrained frequent sets. ACM Transactions on Database Systems (TODS), 28(4), pages 337-389, December 2003.
  7. C.K.-S. Leung, R.T. Ng, and H. Mannila. OSSM: A segmentation approach to optimize frequency counting. In Proceedings of the ICDE 2002, pages 583-592. San Jose, CA, USA, February/March 2002.
  8. C.K.-S. Leung. Data Mining in SQL. Research report, IBM Centre for Advanced Studies (Toronto) & The University of British Columbia, August 2000.
  9. C.K.-S. Leung. Evaluation of Data Mining Opportunities at Workers' Compensation Board. Research report, Workers' Compensation Board of British Columbia (WorkSafeBC) & The University of British Columbia, November 1998.
  10. K.-S. Leung and R. Ng. Multiscale similarity matching for subimage queries of arbitrary size. In Y.E. Ioannidis and W. Klas (eds.), Visual Database Systems 4, Chapter 21, 1998.
Affiliated Web Pages