- 1 J. R. Bach, S. Paul, and R. Jain. A visual information management system for the interactive retrieval of faces. IEEE Transaction on Knowledge and Data Engineering, August 1993.]] Google ScholarDigital Library
- 2 T. Cai, N. Cercone, and J. Han. Attribute-oriented induction in relational databases. In Gregory Piatetsky-Shapiro and William J. Frawley, editors, Knowledge Discovery in Databases, pages 213-228. AAAI Press/The MIT Press, 1991.]]Google Scholar
- 3 David K. Y. Chiu, Andrew K. C. Wong, and Benny Cheung. Information discovery through hierarchical maximum entropy discretization and synthesis. In Gregory Piatetsky-Shapiro and William J. Frawley, editors, Knowledge Discovery in Databases. AAAI Press/The MIT Press, 1991.]]Google Scholar
- 4 Wesley W. Chu, Alfonso F. Cardenas, and Ricky K. Taira. KMeD: A knowledge-based multimedia medical distributed database system Information System, Volume 20, No. 2 pages 75-96, 1995.]] Google ScholarDigital Library
- 5 Wesley W. Chu and Q. Chen. Neighborhood and associative query answering. Journal of Intelligent Information Systems, 1(3/4), 1992.]]Google Scholar
- 6 Wesley W. Chu and Kuorong Chiang. A distribution sensitive clustering method for numerical values. Technical Report 93-0006, UCLA Computer Science Department, 1993.]]Google Scholar
- 7 Wesley W. Chu and Kuorong Chiang. Abstraction of high level concepts from numerical values in databases. In Proceedings of the AAAI Workshop on Knowledge Discovery in Databases, July 1994.]]Google Scholar
- 8 Wesley W. Chu, M. A. Merzbacher, and L. Berkovich. The design and implementation of cobase. In Proceedings of ACM SIGMOD, Washington D. C., USA, May 1993.]] Google ScholarDigital Library
- 9 F. Cuppens and R. Demoloube. Cooperative answering: a methodology to provide intelligent access to databases. In Proceedings of the 2th International Conference on Expert Database Systems, Virginia, USA, 1988.]]Google Scholar
- 10 D. H. Fisher. Knowledge acquisition via incremental conceptual clustering. Machine Learning, 2(2):139-172, 1987.]] Google ScholarDigital Library
- 11 T. Gassterland, P. Godfrey, and Jack Minker. An overview of cooperative answering. Journal of Intelligent Information Systems, 1:123-157, 1992.]]Google ScholarCross Ref
- 12 J. Gennari, P. Langley, and D. Fisher. Models of incremental concept formation. Artificial Intelligence, 40:11-62, 1989.]] Google ScholarDigital Library
- 13 Stephen Jose Hanson and Malcolm Bauer. Conceptual clustering, categorization, and polymorphy. Machine Learning, 3:343-372, 1989.]] Google ScholarDigital Library
- 14 M. Lebowitz. Experiments with incremental conceptual formation. Machine Learning, 2(2):103-138, 1987.]] Google ScholarDigital Library
- 15 R. S. Michalski and R. E. Stepp. Learning from observation: Conceptual clustering. In R. S. Michalski, J. G. Carbonell, and T. M. Mitchell, editors, Machine Learning, volume 1. Margan Kaufmann Publishers, Inc., 1983.]]Google Scholar
- 16 Niblack, Wayne, R. Barber, W. Equitz, M. Flickner, E. Glasman, D. Petkovic, P. Yanker, C. Faloutsos, and G. Taubin. The qbic project: Querying images by content using color, texture, and shape. In Storage and Retrieval for Images and Video Databases, SPIE, volume 1908, 1993.]]Google Scholar
- 17 C. E. Shannon and W. Weaver. The Mathematical Theory of Communication. University of Illinois Press, Urbana, Ill, 1964.]] Google ScholarDigital Library
- 18 L. Shapiro. A structural model of shape. IEEE Transaction on Pattern Analysis and Machine Intelligence, March 1980.]]Google ScholarDigital Library
- 19 P. H. A. Sneath and R. R. Sokal. Numerical Taxonomy: The Principles and Practice of Numerical Classification. W.H.Freeman and Company, San Francisco, 1973.]]Google Scholar
- 20 M Sonka, V. Hlavac, and R. Boyle. Image Processing, Analysis and Machine Vision. Chapman & Hall Computing, 1993.]] Google ScholarDigital Library
- 21 Andrew K. C. Wong and David K. Y. Chiu. Synthesizing statistical knowledge from incomplete mixed-mode data. IEEE Transactions on Pattern Analysis and Machine Intelligence, 9(6):796-805, 1987.]] Google ScholarDigital Library
Index Terms
- An error-based conceptual clustering method for providing approximate query answers
Recommendations
Approximate XML query answers
SIGMOD '04: Proceedings of the 2004 ACM SIGMOD international conference on Management of dataThe rapid adoption of XML as the standard for data representation and exchange foreshadows a massive increase in the amounts of XML data collected, maintained, and queried over the Internet or in large corporate data-stores. Inevitably, this will result ...
Approximate Query Processing with Error Guarantees
Big-Data-Analytics in Astronomy, Science, and EngineeringAbstractIn recent years, with the increase of data and the sophistication of analysis requirements, query processing in databases has become more important. Recently, approximate query processing (AQP) was proposed for efficiently executing database ...
Comments