The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Database Sampling for Data Mining
|
|
Author(s): Patricia E.N. Lutu (University of Pretoria, South Africa)
Copyright: 2005
Pages: 5
Source title:
Encyclopedia of Data Warehousing and Mining
Source Author(s)/Editor(s): John Wang (Montclair State University, USA)
DOI: 10.4018/978-1-59140-557-3.ch066
Purchase
|
Abstract
In data mining, sampling may be used as a technique for reducing the amount of data presented to a data mining algorithm. Other strategies for data reduction include dimension reduction, data compression, and discretisation. For sampling, the aim is to draw, from a database, a random sample, which has the same characteristics as the original database. This chapter looks at the sampling methods that are traditionally available from the area of statistics, how these methods have been adapted to database sampling in general and database sampling for data mining in particular.
Related Content
|
Md Sakir Ahmed, Abhijit Bora.
© 2024.
15 pages.
|
|
Lakshmi Haritha Medida, Kumar.
© 2024.
18 pages.
|
|
Gypsy Nandi, Yadika Prasad.
© 2024.
16 pages.
|
|
Saurav Bhattacharjee, Sabiha Raiyesha.
© 2024.
14 pages.
|
|
Naren Kathirvel, Kathirvel Ayyaswamy, B. Santhoshi.
© 2024.
26 pages.
|
|
K. Sudha, C. Balakrishnan, T. P. Anish, T. Nithya, B. Yamini, R. Siva Subramanian, M. Nalini.
© 2024.
25 pages.
|
|
Sabiha Raiyesha, Papul Changmai.
© 2024.
28 pages.
|
|
|