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Parallel Data Mining in the HYPERBANK Project⋆

  • S. Fotis
  • J. A. Keane
  • R. I. Scott
Conference paper
  • 86 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1685)

Abstract

The aim of the High Performance Banking (HYPERBANK) project is to provide the banking sector with the requisite toolset for the increased understanding of existing and prospective customers. The approach exploits and integrates three areas: business knowledge modelling, data warehousing and data mining, together with parallel computing. Business knowledge modelling formally describes the enterprise in terms of roles, goals and rules. A generic customer-profiling model has been produced and has been instrumental in informing and guiding data mining experiments performed on the banks’ data. Parallel computing is required to manipulate and analyse to maximum effect the vast amounts of data collected by banks. A parallel data warehousing tool has been produced and work is ongoing to integrate the customer profiling model with this tool. In this paper, we present work done in the development and implementation of a variety of parallel data mining techniques.

Keywords

Data Mining Association Rule Parallel Machine Banking Sector Data Warehousing 
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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1999

Authors and Affiliations

  • S. Fotis
    • 1
  • J. A. Keane
    • 1
  • R. I. Scott
    • 1
  1. 1.Department of Computation UMISTUK

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