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Knowledge Discovery: Methods from data mining and machine learning.

Xiaoling Shu1, Yiwan Ye1

  • 1University of California Davis, USA.

Social Science Research
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Knowledge discovery and data mining offer advanced methods for big data analysis, complementing traditional statistics. These techniques uncover hidden patterns and improve predictive models by integrating machine learning with regression analysis.

Keywords:
Knowledge discoverybig datacausal discoverydata miningmachine learningpredition

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Area of Science:

  • Interdisciplinary field encompassing knowledge discovery and data mining.
  • Focus on advanced analytical methods for big data challenges.

Background:

  • Traditional statistical approaches are insufficient for big data.
  • Need for new methods to extract knowledge and discover patterns.
  • Emergence of data mining as a dialectic research process (deductive and inductive).

Purpose of the Study:

  • To explore the role of data mining and machine learning in scientific discovery.
  • To highlight the complementary nature of data mining with traditional statistical methods.
  • To introduce the integration of predictive modeling with parameter estimation regressions.

Main Methods:

  • Utilizing automatic or semi-automatic data mining approaches.
  • Employing machine learning for model building when explicit structures are unclear.
  • Combining predictive modeling with classical parameter estimation regressions.

Main Results:

  • Data mining improves model goodness of fit and reveals hidden patterns.
  • Identifies nonlinear and non-additive effects in data.
  • Enriches scientific discovery by providing insights into data, methods, and theory.

Conclusions:

  • Data mining and machine learning are crucial for extracting knowledge from big data.
  • These methods complement traditional approaches, enhancing both explanation and prediction.
  • The integration of machine learning with regression offers improved analytical models.