Improving Knowledge Discovery through the Integration of Data Mining Techniques

Improving Knowledge Discovery through the Integration of Data Mining Techniques

Muhammad Usman (Shaheed Zulfikar Ali Bhutto Institute of Science and Technology, Pakistan)
Indexed In: SCOPUS
Release Date: August, 2015|Copyright: © 2015 |Pages: 391|DOI: 10.4018/978-1-4666-8513-0
ISBN13: 9781466685130|ISBN10: 1466685131|EISBN13: 9781466685147
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Description

Data warehousing is an important topic that is of interest to both the industry and the knowledge engineering research communities. Both data mining and data warehousing technologies have similar objectives and can potentially benefit from each other’s methods to facilitate knowledge discovery.

Improving Knowledge Discovery through the Integration of Data Mining Techniques provides insight concerning the integration of data mining and data warehousing for enhancing the knowledge discovery process. Decision makers, academicians, researchers, advanced-level students, technology developers, and business intelligence professionals will find this book useful in furthering their research exposure to relevant topics in knowledge discovery.

Topics Covered

The many academic areas covered in this publication include, but are not limited to:

  • Data Mining Techniques
  • Data Warehousing Applications
  • Exploratory Data Analysis
  • Interactive Data Exploration/Visualization and Discovery
  • Knowledge Discovery Framework and Process
  • OLAP Tools
  • Rough Computing

Table of Contents and List of Contributors

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Author(s)/Editor(s) Biography

Dr. Muhammad Usman has completed PhD in Computer & Information Sciences from Auckland University of Technology, New Zealand. He is currently an Associate Professor and Head of Computer Science Department at Shaheed Zulfikar Ali Bhutto Institute of Science and Technology (SZABIST), Islamabad, Pakistan. His research interests include Data Mining, Data Warehousing, Machine Learning, Business Intelligence and Knowledge discovery. He is currently researching in the novel methods and techniques for the seamless integration of Data Mining and Data Warehousing technologies. He has published in international journals and conference proceedings, and he has served as reviewer for a number of premier journals and conferences.

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