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Notice of Retraction
Mining Top-k Fault Tolerant Association Rules by Redundant Pattern Disambiguation in Data Streams

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4 Author(s)
You Yuyang ; Coll. of Comput. Sci. & Technol., Harbin Eng. Univ., Harbin, China ; Zhang Jianpei ; Yang Zhihong ; Liu Guocai

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.

The real-world data may be usually polluted by uncontrolled factors or contained with noisy. Fault-tolerant frequent pattern can overcome this problem. It may express more generalized information than frequent pattern which is absolutely matched. The present research is integrated with previous research into an integrity new method, called Top-NFTDS, to discover fault-tolerant association rules over stream. It can discover top-k true fault-tolerant rules without minimum support threshold and minimum confidence threshold specified by user. We extend the negative itemsets to fault-tolerant space and disambiguate redundant patterns by this algorithm. Experiment results show that the developed algorithm is an efficient method for mining top-k fault-tolerant association rules in data streams.

Published in:

Intelligent Computing and Cognitive Informatics (ICICCI), 2010 International Conference on

Date of Conference:

22-23 June 2010