Notice of RetractionResearch of enterprise crisis alert by data mining techniques based on rough set

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.

Facing the drastic market competition and complex environment, the enterprise often meets kinds of crisis, therefore, they need building crisis alert system so as to summarize experience, improve ability of resisting risk and keep themselves develop persistently. This paper gave a classification algorithm by attribute importance (CAAI algorithm). Attributes are reduced by rough set theory, redundant attributes are removed and the core attributes are gained. When building the decision tree through the CAAI algorithm, the current node was chosen from the core attributes of the simplified decision table and decision tree splitting is according to the importance degree of attribute so as to reduce computation and gain relative simple classification rules. An example in cheat crisis alert is given to validate the CAAI algorithm. The results show that the method is effective. The research lays a foundation for further study on enterprise crisis alert system.