DocumentCode
2825542
Title
Improving KNN based text classifications
Author
Jiang, Zongli ; Deng, Yi
Author_Institution
Sch. of Comput. Sci. & Eng., Beijing Univ. of Technol., Beijing, China
Volume
2
fYear
2010
fDate
21-24 May 2010
Abstract
In a variety of text classification algorithm, KNN is a competitive one with simple implementation and high efficiency. However, with the expansion of the size of the text, the runtime of KNN will grow rapidly that cannot be afford. In this paper, we improve the KNN by introducing the kd-tree storage structure and reducing the sample space through the sample clustering methods. And experiment shows that the runtime of improved KNN algorithm reduce apparently.
Keywords
pattern classification; pattern clustering; text analysis; tree data structures; KNN algorithm; kd-tree storage structure; sample clustering methods; text classification algorithm; Classification algorithms; Classification tree analysis; Clustering algorithms; Computer science; Databases; Information retrieval; Natural languages; Nearest neighbor searches; Runtime; Text categorization; KNN; classification algorithm; clustering;
fLanguage
English
Publisher
ieee
Conference_Titel
Future Computer and Communication (ICFCC), 2010 2nd International Conference on
Conference_Location
Wuhan
Print_ISBN
978-1-4244-5821-9
Type
conf
DOI
10.1109/ICFCC.2010.5497421
Filename
5497421
Link To Document