Document Clustering Based on Modified Artificial Immune Network

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 ve...

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Bibliographic Details
Published inLecture notes in computer science pp. 516 - 521
Main Authors Xu, Lifang, Mo, Hongwei, Wang, Kejun, Tang, Na
Format Book Chapter Conference Proceeding
LanguageEnglish
Published Berlin, Heidelberg Springer Berlin Heidelberg 2006
Springer
SeriesLecture Notes in Computer Science
Subjects
Applied sciences
artificial immune network
Artificial immune system
Artificial intelligence
Computer science; control theory; systems
document clustering
Exact sciences and technology
Cluster analysis
Rough set theory
Classification
K means algorithm
Binary code
Artificial intelligence
Principal component analysis
Immune system
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Summary: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.
ISBN:9783540362975
3540362975
ISSN:0302-9743
1611-3349
DOI:10.1007/11795131_75