Title :
Improving KNN based text classifications
Author :
Jiang, Zongli ; Deng, Yi
Author_Institution :
Sch. of Comput. Sci. & Eng., Beijing Univ. of Technol., Beijing, China
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;
Conference_Titel :
Future Computer and Communication (ICFCC), 2010 2nd International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-5821-9
DOI :
10.1109/ICFCC.2010.5497421
