Towards A Reusable And Extensible Adapter Framework For A Data Mining Middleware

• Autores: Lai Ee Hen, Sai Peck Lee
• Localización: International journal of the computer, the internet and management, ISSN 0858-7027, Vol. 16, Nº. 1 (ENE-ABR), 2008, págs. 61-69
• Idioma: inglés
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• Resumen

  • Data mining has been an effective technique in helping organizations to uncover hidden patterns within the organization’s data in order to gain a competitive edge. With the potential opportunities data mining could bring, it has led to an increasing demand of data mining tools. Data mining tools often face challenges to be designed to cater for a vast option of data sources, data mining techniques and reporting formats in order to support the dynamic changing requirements. New data mining techniques, data sources or reporting may not be able to be supported by some existing data mining tools. Therefore, to support this wide spectrum of options, a data mining tool needs to be designed to be extensible and reusable. Hence, we propose an architecture of a reusable and extensible data mining middleware that supports a wide spectrum of data sources, data mining techniques and reports to help organizations to in decision support.