Study on topic tracking system based on KNN

Abstract

Text classification is the key technology for topic tracking, and vector space model (VSM) is one of the most simple and effective topics representation model. Feature selection algorithm in VSM is an important means of data pre-processing, and it can reduce vector space dimension and improve the generalization ability of the algorithm. Therefore, it is necessary for feature selection algorithms to be in-depth and extensive research. So we develop a topic tracking system to study how feature dimension and the value of K-neighbors affect topic tracking. Then we get the variation law that they affect topic tracking, and add up their optimal values in topic tracking. Finally, TDT evaluation methods prove that optimal topic tracking performance based on adjusting the value of K-neighbors for text increases by 7.246% more than feature dimension.