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.
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© 2006 Springer-Verlag Berlin Heidelberg
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Xu, L., Mo, H., Wang, K., Tang, N. (2006). Document Clustering Based on Modified Artificial Immune Network. In: Wang, GY., Peters, J.F., Skowron, A., Yao, Y. (eds) Rough Sets and Knowledge Technology. RSKT 2006. Lecture Notes in Computer Science(), vol 4062. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11795131_75
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DOI: https://doi.org/10.1007/11795131_75
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-36297-5
Online ISBN: 978-3-540-36299-9
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