Notice of RetractionThe application of improved K-Nearest Neighbor classification in topic tracking

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.

News topic tracking is one of the major tasks of TDT, the aim of which is monitoring news story flow as well as recognizing and giving subsequent stories in advance related to topics described by several news stories. This paper explains in details the concept of topic tracking and the common methods at present; aiming at the scarcity of positive example of training, this paper makes effective improvement on traditional KNN taxonomy and applies it to topic tracking; besides, it adds time window strategy to the process of topic tracking, which effectively reduces calculation complexity. The final experimental result also proves that this method is superior to traditional KNN topic tracking method.