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Data Mining and Data Fusion for Enhanced Decision Support

In: Handbook on Decision Support Systems 1

Author

Listed:
  • Shiraj Khan

    (University of South Florida)

  • Auroop R. Ganguly

    (Oak Ridge National Laboratory, Oak)

  • Amar Gupta

    (University of Arizona)

Abstract

The process of data mining converts information to knowledge by using tools from the disciplines of computational statistics, database technologies, machine learning, signal processing, nonlinear dynamics, process modeling, simulation, and allied disciplines. Data mining allows business problems to be analyzed from diverse perspectives, including dimensionality reduction, correlation and co-occurrence, clustering and classification, regression and forecasting, anomaly detection, and change analysis. The predictive insights generated from data mining can be further utilized through real-time analysis and decision sciences, as well as through human-driven analysis based on management by exceptions or objectives, to generate actionable knowledge. The tools that enable the transformation of raw data to actionable predictive insights are collectively referred to as decision support tools. This chapter presents a new formalization of the decision process, leading to a new decision superiority model, partially motivated by the joint directors of laboratories (JDL) data fusion model. In addition, it examines the growing importance of data fusion concepts.

Suggested Citation

  • Shiraj Khan & Auroop R. Ganguly & Amar Gupta, 2008. "Data Mining and Data Fusion for Enhanced Decision Support," International Handbooks on Information Systems, in: Handbook on Decision Support Systems 1, chapter 27, pages 581-608, Springer.
    • Handle: RePEc:spr:ihichp:978-3-540-48713-5_27
    DOI: 10.1007/978-3-540-48713-5_27
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