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Notice of Retraction
A WSRF-enabled distributed data mining approach to clustering WEKA4WS -based

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5 Author(s)
Ren Zai-an ; Nanjing Artillery Acad. of the P.L.A, Nanjing, China ; Wang Bin ; Zheng Shi-ming ; Miao Zhuang
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Notice of Retraction

After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles.

We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.

The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.

Weka4WS adopts the WSRF technology for implementing remote data mining algorithms and dealing with distributed computation, a WSRF-compliant Web service is used to carry out all the data mining algorithms provided by the Weka library. This paper describes Weka4WS, a framework that extends the widely used open source Weka toolkit to support distributed data mining on WSRF-enabled Grids and have a try at solving the problem of distributed clustering, in addition, introduces the concepts of Admixture Probability, and achieves the distributed clustering algorithm with Weka Library, designs a distributed data mining architecture oriented-services in grid environment combining grid with web services, the implementation of Weka4WS using the WSRF libraries and services provided by Globus Toolkit 4. Finally it validates the validity of the algorithm and the feasibility of the architecture with the distributed clustering based on WEKA4WS.

Published in:

Web Society (SWS), 2010 IEEE 2nd Symposium on

Date of Conference:

16-17 Aug. 2010