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http://dx.doi.org/10.3745/KIPSTB.2004.11B.7.849

An Efficient kNN Algorithm  

Lee Jae Moon (한성대학교 컴퓨터공학부)
Abstract
This paper proposes an algorithm to enhance the execution time of kNN in the document classification. The proposed algorithm is to enhance the execution time by minimizing the computing cost of the similarity between two documents by using the list of pairs, while the conventional kNN uses the iist of pairs. The 1ist of pairs can be obtained by applying the matrix transposition to the list of pairs at the training phase of the document classification. This paper analyzed the proposed algorithm in the time complexity and compared it with the conventional kNN. And it compared the proposed algorithm with the conventional kNN by using routers-21578 data experimentally. The experimental results show that the proposed algorithm outperforms kNN about $90{\%}$ in terms of the ex-ecution time.
Keywords
Document Classification; Training Document; Testing Document; kNN; NaiveBayes; SVM; Document Vector;
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Times Cited By KSCI : 1  (Citation Analysis)
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