Notice of RetractionFusion Algorithms of Membership Based on Data-Mining

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.

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Existing fusion algorithms of membership are questionable because they can´t distinguish the useful or useless part in indexes membership. As a result, redundant data, which are useless to objective classification, are involved in calculation of object membership. In order to eliminate the interference of redundant data in fusion algorithms of membership, through excavating the information of the object classification that hides in index membership to eliminate the redundant data using data excavation method based on entropy; and extract effective value; Then transform effective value to comparable value; At last define object membership by comparable sum. Based on this, a fusion algorithm of information based on membership can be realized, and illustrate the algorithm application by a case.