Domain Driven Intelligent Knowledge Discovery

Yong Shi (), Lingling Zhang, Yingjie Tian () and Xingsen Li
Additional contact information
Yong Shi: Chinese Academy of Sciences
Lingling Zhang: University of Chinese Academy of Sciences
Yingjie Tian: Chinese Academy of Sciences
Xingsen Li: Zhejiang University

Chapter 4 in Intelligent Knowledge, 2015, pp 47-80 from Springer

Abstract: Abstract Data mining algorithms, making use of powerful computation ability of computers, can make up the weakness of logical computation of human and extract novel, interesting, potentially useful and finally understandable knowledge. As a main way to acquire knowledge from data and information, data mining algorithms can generate knowledge that cannot be obtained from experts, thus become a new way to assist decision makings. As the critical technology of knowledge acquisition and the key element of business intelligence, data mining has been a hot research area over the last several decades and made a great progress. Scholars in this area proposed many popular benchmark algorithms and extensions, and applied them in many applications ranging from banking, insurance industries to retail industry.

Keywords: Data Mining; Association Rule; Domain Knowledge; Association Rule Mining; Data Mining Algorithm (search for similar items in EconPapers)
Date: 2015
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:spbrcp:978-3-662-46193-8_4

Ordering information: This item can be ordered from
http://www.springer.com/9783662461938

DOI: 10.1007/978-3-662-46193-8_4

Access Statistics for this chapter

More chapters in SpringerBriefs in Business from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().