Data Mining and Knowledge Management

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 1 in Intelligent Knowledge, 2015, pp 1-11 from Springer

Abstract: Abstract Data mining (DM) is a powerful information technology (IT) tool in today’s competitive business world, especially as our human society entered the Big Data era. From academic point of view, it is an area of the intersection of human intervention, machine learning, mathematical modeling and databases. In recent years, data mining applications have become an important business strategy for most companies that want to attract new customers and retain existing ones. Using mathematical techniques, such as, neural networks, decision trees, mathematical programming, fuzzy logic and statistics, data mining software can help the company discover previously unknown, valid, and actionable information from various and large sources (either databases or open data sources like internet) for crucial business decisions. The algorithms of the mathematical models are implemented through some sort of computer languages, such as C++, JAVA, structured query language (SQL), on-line analysis processing (OLAP) and R. The process of data mining can be categorized as selecting, transforming, mining, and interpreting data. The ultimate goal of doing data mining is to find knowledge from data to support user’s decision. Therefore, data mining is strongly related with knowledge and knowledge management.

Keywords: Data Mining; Association Rule; Knowledge Management; Structure Query Language; Data Mining Process (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_1

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

DOI: 10.1007/978-3-662-46193-8_1

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 ().