IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Measuring the Effects of Data Mining on Inference

Measuring the Effects of Data Mining on Inference
View Sample PDF
Author(s): Tom Burr (Statistical Sciences, Los Alamos National Laboratory, USA) and S. Tobin (Nuclear Nonproliferation and Security, Los Alamos National Laboratory, USA)
Copyright: 2015
Pages: 9
Source title: Encyclopedia of Information Science and Technology, Third Edition
Source Author(s)/Editor(s): Mehdi Khosrow-Pour, D.B.A. (Information Resources Management Association, USA)
DOI: 10.4018/978-1-4666-5888-2.ch176

Purchase

View Measuring the Effects of Data Mining on Inference on the publisher's website for pricing and purchasing information.

Abstract

Data mining is a term used to describe various types of exploratory data analysis whose purposes are to select data models, estimate model parameters, and generate hypotheses that can be tested on future data. It is known that model predictions are overly optimistic when generated from the same data that are used to select a model and estimate its parameters. Therefore, most statistical procedures assume that the data model is selected prior to data collection. Alternatively, to adjust for data mining, we describe steps that should be taken to account for “choosing the best” among many candidate models.

Related Content

Jianping Peng, Jing ("Jim") Quan, Guoying Zhang, Alan J. Dubinsky. © 2019. 20 pages.
Rezvan Hosseingholizadeh, Hadi El-Farr, Somayyeh Ebrahimi Koushk Mahdi. © 2019. 28 pages.
Zbigniew Mikolajuk. © 2019. 18 pages.
Ramon Visaiz, Andrea M Skinner, Spencer Wolfe, Megan Jones, Ashley Van Ostrand, Antonio Arredondo, J. Jacob Jenkins. © 2019. 22 pages.
Badreya Al-Jenaibi. © 2019. 19 pages.
Ping-Yu Chang. © 2019. 16 pages.
Mohammadhossein Barkhordari, Mahdi Niamanesh, Parastoo Bakhshmandi. © 2019. 38 pages.
Body Bottom