Document Clustering Based on Modified Artificial Immune Network | Zendy

Lifang Xu | Zendy; Hongwei Mo | Zendy; Kejun Wang | Zendy; Na Tang | Zendy

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Document Clustering Based on Modified Artificial Immune Network

Author(s) -

Lifang Xu,

Hongwei Mo,

Kejun Wang,

Na Tang

Publication year - 2006

Publication title -

lecture notes in computer science

Language(s) - English

Resource type - Book series

eISSN - 1611-3349

pISSN - 0302-9743

ISBN - 3-540-36297-5

DOI - 10.1007/11795131_75

Subject(s) - computer science , cluster analysis , feature vector , feature (linguistics) , artificial intelligence , pattern recognition (psychology) , artificial immune system , principal component analysis , dimension (graph theory) , dimensionality reduction , mathematics , philosophy , linguistics , pure mathematics

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

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