Advertisement

Role of Human Intelligence in Domain Driven Data Mining

  • Sumana Sharma
  • Kweku-Muata Osei-Bryson

Data Mining is an iterative, multi-step process consisting of different phases such as domain (or business) understanding, data understanding, data preparation, modeling, evaluation and deployment. Various data mining tasks are dependent on the human user for their execution. These tasks and activities that require human intelligence are not amenable to automation like tasks in other phases such as data preparation or modeling are. Nearly all Data Mining methodologies acknowledge the importance of the human user but do not clearly delineate and explain the tasks where human intelligence should be leveraged or in what manner. In this chapter we propose to describe various tasks of the domain understanding phase which require human intelligence for their appropriate execution.

Keywords

Data Mining Success Criterion Human User Business Objective Business User 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Cao, L. and C. Zhang (2006). “Domain-Driven Data Mining: A Practical Methodology.” International Journal of Data Warehousing and Mining 2(4): 49–65.Google Scholar
  2. 2.
    Berry, M. and G. Linoff (2000). Mastering Data Mining: The Art and Relationship of Customer Relationship Management, John Wiley and SonsGoogle Scholar
  3. 3.
    Cabena, P., P. Hadjinian, et al. (1998). Discovering Data Mining: From Concepts to Implementation., Prentice Hall.Google Scholar
  4. 4.
    Cios, K. and L. Kurgan (2005). Trends in Data Mining and Knowledge Discovery. Advanced Techniques in Knowledge Discovery and Data Mining. N. Pal and L. Jain, Springer: 1–26.Google Scholar
  5. 5.
    CRISP-DM. (2003). “Cross Industry Standard Process for Data Mining 1.0: Step by Step Data Mining Guide.” Retrieved 01/10/07, from http://www.crisp-dm.org/
  6. 6.
    Fayyad, U., G. Paitetsky-Shapiro, et al. (1996). “The KDD process for extracting useful knowledge from volumes of data.” Communications of the ACM 39(11): 27–34.CrossRefGoogle Scholar
  7. 7.
    Markus, M. L. (2001). “Toward a Theory of Knowledge Reuse: Types of Knowledge Reuse Situations and Factors in Reuse Success.” Journal of Management Information Systems 18(1): 57–94.MathSciNetGoogle Scholar
  8. 8.
    Osei-Bryson, K.-M. (2004). “Evaluation of Decision Trees.” Computers and Operations Research 31: 1933–1945.MATHCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.Virginia Commonwealth UniversityVirginiaUSA

Personalised recommendations