David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Ezio Di Nucci
Jack Alan Reynolds
Learn more about PhilPapers
Journal of Economic Methodology 7 (2):231-264 (2000)
The primary objective of this paper is to revisit a number of empirical modelling activities which are often characterized as data mining, in an attempt to distinguish between the problematic and the non-problematic cases. The key for this distinction is provided by the notion of error-statistical severity. It is argued that many unwarranted data mining activities often arise because of inherent weaknesses in the Traditional Textbook (TT) methodology. Using the Probabilistic Reduction (PR) approach to empirical modelling, it is argued that the unwarranted cases of data mining can often be avoided by dealing directly with the weaknesses of the TT approach. Moreover, certain empirical modelling activities, such as diagnostic testing and data snooping, constitute legitimate procedures in the context of the PR approach.
|Keywords||No keywords specified (fix it)|
|Categories||categorize this paper)|
Setup an account with your affiliations in order to access resources via your University's proxy server
Configure custom proxy (use this if your affiliation does not provide a proxy)
|Through your library|
References found in this work BETA
No references found.
Citations of this work BETA
Aris Spanos (2007). Curve Fitting, the Reliability of Inductive Inference, and the Error-Statistical Approach. Philosophy of Science 74 (5):1046-1066.
Similar books and articles
Dinah Payne & Cherie Courseault Trumbach (2009). Data Mining: Proprietary Rights, People and Proposals. Business Ethics 18 (3):241-252.
Kevin D. Hoover & Stephen J. Perez (2000). Three Attitudes Towards Data Mining. Journal of Economic Methodology 7 (2):195-210.
Herman T. Tavani (1999). Informational Privacy, Data Mining, and the Internet. Ethics and Information Technology 1 (2):137-145.
Clinton A. Greene (2000). I Am Not, nor Have I Ever Been a Member of a Data-Mining Discipline. Journal of Economic Methodology 7 (2):217-230.
Roger E. Backhouse & Mary S. Morgan (2000). Introduction: Is Data Mining a Methodological Problem? Journal of Economic Methodology 7 (2):171-181.
Steven Cook (2001). Observations on the Practice of Data-Mining: Comments on the JEM Symposium. Journal of Economic Methodology 8 (3):415-419.
Herman T. Tavani (1999). KDD, Data Mining, and the Challenge for Normative Privacy. Ethics and Information Technology 1 (4):265-273.
Anthony Danna & Oscar H. Gandy (2002). All That Glitters is Not Gold: Digging Beneath the Surface of Data Mining. [REVIEW] Journal of Business Ethics 40 (4):373 - 386.
Adrian R. Pagan & Michael R. Veall (2000). Data Mining and the Econometrics Industry: Comments on the Papers of Mayer and of Hoover and Perez. Journal of Economic Methodology 7 (2):211-216.
Lita van Wel & Lambèr Royakkers (2004). Ethical Issues in Web Data Mining. Ethics and Information Technology 6 (2):129-140.
Kamal Dahbur & Thomas Muscarello (2003). Classification System for Serial Criminal Patterns. Artificial Intelligence and Law 11 (4):251-269.
Todd Harris (2003). Data Models and the Acquisition and Manipulation of Data. Philosophy of Science 70 (5):1508-1517.
Added to index2012-02-20
Total downloads16 ( #235,128 of 1,911,080 )
Recent downloads (6 months)7 ( #96,280 of 1,911,080 )
How can I increase my downloads?