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Data Mining and Knowledge Management

In: Intelligent Knowledge

Author

Listed:
  • Yong Shi

    (Chinese Academy of Sciences)

  • Lingling Zhang

    (University of Chinese Academy of Sciences)

  • Yingjie Tian

    (Chinese Academy of Sciences)

  • Xingsen Li

    (Zhejiang University)

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.

Suggested Citation

  • Yong Shi & Lingling Zhang & Yingjie Tian & Xingsen Li, 2015. "Data Mining and Knowledge Management," SpringerBriefs in Business, in: Intelligent Knowledge, edition 127, chapter 1, pages 1-11, Springer.
    • Handle: RePEc:spr:spbrcp:978-3-662-46193-8_1
    DOI: 10.1007/978-3-662-46193-8_1
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