Multi-Layered Framework for Distributed Data Mining

نویسندگان

  • Masum Serazi
  • Amal Perera
  • Qiang Ding
  • Vasiliy Malakhov
  • William Perrizo
چکیده

There is an increase in the demand for data mining applications on the web. With the increase in the size of data sets there is also a demand for scalable generic solutions. Scalability and generic data mining models can be provided with the use of distributed computing. In this paper we propose the use of a multilayered framework for a distributed data mining system. A multi-layered architecture can take advantage of the latest technological advances in hardware to provide efficient solutions and also allow the easy addition of new data mining and data capture components to the basic system. A multi-layered architecture also facilitates an iterative development process. In this paper we show the use of a high end data mining engine with generic data mining models on the server side and a client that can capture the client requirements over the web.

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تاریخ انتشار 2004