@INPROCEEDINGS { Rsds4271,
author = {Xu, Lifang and Mo, Hongwei and Wang, Kejun and Tang, Na}, 
title = {Document Clustering Based on Modified Artificial Immune Network}, 
booktitle = {Rough Sets and Knowledge Technology}, 
conference = {Rough Sets and Knowledge Technology}, 
series =  = {Lecture Notes in Computer Science}, 
volume = 4062, 
pages = {516--521}, 
publisher = {Springer-Verlag}, 
address = {Heidelberg - Berlin, Germany}, 
year = 2006, 
editor = {Wang, Guoying and Peters, James F. and Skowron, Andrzej and Yao, Yiyu}, 
isbn = {978-3-540-36297-5}, 
abstract = {The aiNet is one of artificial immune system algorithms which exploits the features of nature immune system. In this paper, aiNet is modified by integrating K-means and Principal Component Analysis and used to more complex tasks of document clustering. The results of using different coded feature vectorsâ€“binary feature vectors and real feature vectors for documents are compared. PCA is used as a way of reducing the dimension of feature vectors. The results show that it can get better result by using aiNet with PCA and real feature vectors.}, 
keywords = {artificial immune system, artificial immune network, document clustering, }, 
}