Hostname: page-component-78c5997874-ndw9j Total loading time: 0 Render date: 2024-11-05T21:12:03.305Z Has data issue: false hasContentIssue false
  • English
  • Français

Intelligent data analysis: issues and challenges

Published online by Cambridge University Press:  07 July 2009

Xiaohui Liu
Xiaohui Liu
Affiliation:
Department of Computer Science, Birkbeck College, University of London, Malet Street, London WCIE 7HX, UK. Email: [email protected]
Get access Rights & Permissions [Opens in a new window]

Extract

Two phenomena have probably affected modern data analysts' lives more than anything else. First, the size of real-world data sets is getting increasingly large, especially during the last decade or so. Second, modern computational methods and tools are being developed which add further capability to traditional statistical analysis tools. These two developments have created a new range of problems and challenges for analysts, as well as new opportunities for intelligent systems in data analysis.

Type
Research Article
Information
The Knowledge Engineering Review , Volume 11 , Issue 4 , December 1996 , pp. 365 - 371
Copyright
Copyright © Cambridge University Press 1996

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Brodley, CE and Smyth, P, 1996. “Applying classification algorithms in practice” Statistics and Computing (in press).Google Scholar
Chatfield, C, 1988. Problem Solving: a Statistician's Guide Chapman & Hall.CrossRefGoogle Scholar
Cohen, P, 1995. Empirical Methods for Artficial Intelligence MIT Press.Google Scholar
Elder, IV J and Pregibon, D, 1996. “A statistical perspective on knowledge discovery in databases” In Fayyad, UM, Piatetsky-Shapiro, G, Smyth, P and Uthurusamy, R (eds), Advances in Knowledge Discóvery and Data Mining AAAI/MIT Press.Google Scholar
Hand, DJ, 1996. “Intelligent data analysis and deep understanding” Proc. Intelligent Data Management 96 Unicom, London, pp. 26–39.Google Scholar
Liu, X, Cheng, G and Wu, J, 1994. “Noise and uncertainty management in intelligent data modeling” Proc. AAAI-94 Seattle, WA, pp. 263–268.Google Scholar
Michie, D, Spiegelhalter, DJ and Taylor, CC, (eds) 1994. Machine Learning, Neural and Statistical Classficalion Ellis Horwood.Google Scholar
Nakhaeizadeh, G, 1995. “What Daimler-Benz has learned as an industrial partner from the machine learning project StatLog?” Proc. Workshop on Applying Machine Learning in Practice 22–26.Google Scholar
Piatetsky-Shapiro, G, Brachman, R, Khabaza, T, Kloesgen, W and Simoudis, E, 1996. “An overview of issues in developing industrial data mining and knowledge discovery applications” In Simoudis, E, Han, J and Fayyad, U, (eds) Proc. Second International Conference on Knowledge Discovery and Data Mining AAAI Press.Google Scholar
Tukey, JW, 1977. Exploratory Data Analysis Addison-Wesley.Google Scholar
Weiss, SM and Kulikowski, CA, 1991. Computer Systems that Learn Morgan Kaufmann.Google Scholar