Document clustering based on modified artificial immune network

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 vectors-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. © Springer-Verlag Berlin Heidelberg 2006.