Notice of RetractionMining FCI of incremental attribute in Iceberg concept lattice

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.

Frequent closed itemsets is a smaller, representative of the frequent itemsets. Some existed algorithms for mining frequent closed itemsets based on FP-Tree improvement or incremental mining the database with objects, thus, mining frequent closed itemsets inefficient in the database of increasing attribute. This paper, based on Iceberg concept lattice model, presents a frequent closed itemsets mining algorithm FCI-AI in the Iceberg concept lattice. Adding an attribute to database, our algorithm can use the original Iceberg concept lattice for mining the frequent closed itemsets incrementally without having to mine all frequent closed itemsets from scratch. The experimental results show that proposed FCI-AI algorithm with the advantage of less repetitive tasks and efficiency.