Notice of RetractionWeighted frequent patterns mining over data streams

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 patterns mining is an important data mining task with many real-world applications. By considering different weights of the items, weighted frequent pattern mining can discover more important knowledge compared to traditional frequent patterns mining. In this paper, we presented a new algorithm called SMFPM to discover weighted frequent patterns over data streams, the proposed method is based on slide windows where stream data is break into batches and only process each batch once, experimental results show that SMFPM is efficient for weighted frequent patterns mining, and it outperforms WFPMDS which is another algorithm for efficient mining weighted frequent patterns in terms of execution time.