NDLI: A Fast KNN Algorithm for Text Categorization

Content Provider	IEEE Xplore Digital Library
Author	Yu Wang Zheng-Ou Wang
Copyright Year	2007
Description	Author affiliation: Hebei Univ., Baoding (Yu Wang)
Abstract	The KNN algorithm applied to text categorization is a simple, valid and non-parameter method. The traditional KNN has a fatal defect that the time of similarity computing is huge. The practicality will be lost when the KNN algorithm is applied to text categorization with the high dimension and huge samples. In this paper, a method called TFKNN(Tree-Fast-K-Nearest-Neighbor) is presented, which can search the exact k nearest neighbors quickly. In the method, a SSR tree for searching K nearest neighbors is created, in which all child nodes of each non-leaf node are ranked according to the distances between their central points and the central point of their parent. Then the searching scope is reduced based on the tree. Subsequently , the time of similarity computing is decreased largely.
Starting Page	3436
Ending Page	3441
File Size	656724
Page Count	6
File Format	PDF
ISBN	9781424409723
DOI	10.1109/ICMLC.2007.4370742
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-08-19
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Text categorization Nearest neighbor searches Machine learning algorithms Machine learning Cybernetics Mathematics Computer science Systems engineering and theory Web sites Support vector machines SSR-tree KNN Similarity
Content Type	Text
Resource Type	Article

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