Knowledge Discovery Process and Methods to Enhance Organizational Performance

Kweku-Muata Osei-Bryson, Corlane Barclay

March 16, 2015 by Auerbach Publications
Reference - 404 Pages - 69 B/W Illustrations
ISBN 9781482212365 - CAT# K21714

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Features

  • Explains the knowledge discovery and data mining (KDDM) process in a manner that makes it easy to understand and implement
  • Discusses the implications of data mining, including economic, security, privacy, ethical, and legal considerations
  • Includes case study examples of KDDM in businesses and governments
  • Details key requirements for developing robust data mining objectives that are aligned with strategic business objectives
  • Details critical success factors for KDDM projects as well as the impact of poor quality data or inaccessibility to data on KDDM projects

Summary

Although the terms "data mining" and "knowledge discovery and data mining" (KDDM) are sometimes used interchangeably, data mining is actually just one step in the KDDM process. Data mining is the process of extracting useful information from data, while KDDM is the coordinated process of understanding the business and mining the data in order to identify previously unknown patterns.

Knowledge Discovery Process and Methods to Enhance Organizational Performance explains the knowledge discovery and data mining (KDDM) process in a manner that makes it easy for readers to implement. Sharing the insights of international KDDM experts, it details powerful strategies, models, and techniques for managing the full cycle of knowledge discovery projects. The book supplies a process-centric view of how to implement successful data mining projects through the use of the KDDM process. It discusses the implications of data mining including security, privacy, ethical and legal considerations.

  • Provides an introduction to KDDM, including the various models adopted in academia and industry
  • Details critical success factors for KDDM projects as well as the impact of poor quality data or inaccessibility to data on KDDM projects
  • Proposes the use of hybrid approaches that couple data mining with other analytic techniques (e.g., data envelopment analysis, cluster analysis, and neural networks) to derive greater value and utility
  • Demonstrates the applicability of the KDDM process beyond analytics
  • Shares experiences of implementing and applying various stages of the KDDM process in organizations

The book includes case study examples of KDDM applications in business and government. After reading this book, you will understand the critical success factors required to develop robust data mining objectives that are in alignment with your organization’s strategic business objectives.

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