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International Journal of Knowledge Management Studies
International Journal of Knowledge Management Studies


Special Issue on: “Data Mining: Intelligent Data Discovery and Knowledge Management”

Guest Editors:
Professor Ali K. Kamrani and Professor Hamid R. Parsaei, University of Houston, USA
Mahammad Aloudat, Baker Oil Tools, USA

During the last few years, data mining has received increasing attention from different fields, but especially from the business community. This commercial interest has grown mainly because of the awareness of companies that the vast amounts of data collected from customers and their behaviours contain valuable information. If this information can be somehow made explicit, it will be available to improve various business processes.

Data mining deals with the discovery of hidden knowledge, unexpected patterns and new rules from large databases. It is regarded as the key element of a much more elaborate process called knowledge discovery in databases, or KDD, which is closely linked to data warehousing. Data mining can bring significant gains to organisations, for example through better-targeted marketing and enhanced internal performance. The long-term goal of data mining is to create a self-learning organisation that makes optimal use of the information it generates.

The goal of this special issue is to cover a variety of topics and issues related to data mining with specific application to knowledge discovery. Each manuscript will be reviewed; the reviewers will be from a pool of academic and practitioners who have demonstrated to have worked in similar problem areas. The targeted readerships are professional, technical and academic (research directors, managers, professors, research associates, etc.)

Subject Coverage
Topics include but are not limited to:
  • Data mining process
    • Pre-processing and data size reduction
    • Data selection and transformation
    • Analysis and Interpretations
    • Post-processing of results
  • Statistics and probability analysis in data mining
  • Data and knowledge representation for data mining
  • Knowledge discovery
  • Neural networks, fuzzy logic, and genetic algorithm in data mining
  • Integration of data warehousing, OLAP and data mining
  • Quality assessment
    • Complexity and efficiency analysis
    • Risk management
    • Results analysis and assessment
  • Applications
    • Classification and clustering
    • Pattern recognition and association analysis
    • E-commerce, manufacturing, design, marketing, quality conformances, healthcare, telecommunications, etc.
    • Case studies and other related topics

    Notes for Prospective Authors

    Submitted papers should not have been previously published nor be currently under consideration for publication elsewhere

    All papers are refereed through a peer review process. A guide for authors, sample copies and other relevant information for submitting papers are available on the Author Guidelines page

    Important Dates

    Full paper submission: 31 January, 2007

    Notification of reviews: 15 March, 2007

    Revised manuscript submission: 16 April, 2007

    Notification of acceptance: 30 April, 2007

    Final paper: 14 May, 2007