The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Association Rules and Statistics
|
|
Author(s): Martine Cadot (University of Henri Poincaré/LORIA, Nancy, France), Jean-Baptiste Maj (LORIA/INRIA, France)and Tarek Ziadé (NUXEO, France)
Copyright: 2009
Pages: 4
Source title:
Encyclopedia of Data Warehousing and Mining, Second Edition
Source Author(s)/Editor(s): John Wang (Montclair State University, USA)
DOI: 10.4018/978-1-60566-010-3.ch016
Purchase
|
Abstract
A manager would like to have a dashboard of his company without manipulating data. Usually, statistics have solved this challenge, but nowadays, data have changed (Jensen, 1992); their size has increased, and they are badly structured (Han & Kamber, 2001). A recent method—data mining—has been developed to analyze this type of data (Piatetski-Shapiro, 2000). A specific method of data mining, which fits the goal of the manager, is the extraction of association rules (Hand, Mannila & Smyth, 2001). This extraction is a part of attribute-oriented induction (Guyon & Elisseeff, 2003). The aim of this paper is to compare both types of extracted knowledge: association rules and results of statistics.
Related Content
|
Girija Ramdas, Irfan Naufal Umar, Nurullizam Jamiat, Nurul Azni Mhd Alkasirah.
© 2024.
18 pages.
|
|
Natalia Riapina.
© 2024.
29 pages.
|
|
Xinyu Chen, Wan Ahmad Jaafar Wan Yahaya.
© 2024.
21 pages.
|
|
Fatema Ahmed Wali, Zahra Tammam.
© 2024.
24 pages.
|
|
Su Jiayuan, Jingru Zhang.
© 2024.
26 pages.
|
|
Pua Shiau Chen.
© 2024.
21 pages.
|
|
Minh Tung Tran, Thu Trinh Thi, Lan Duong Hoai.
© 2024.
23 pages.
|
|
|