期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2016
卷号：7
期号：3
DOI：10.14569/IJACSA.2016.070333
出版社：Science and Information Society (SAI)
摘要：Kohonen self-organization algorithm, known as “topologic maps algorithm”, has been largely used in many applications for classification. However, few theoretical studies have been proposed to improve and optimize the learning process of classification and clustering for dynamic and scalable systems taking into account the evolution of multi-parameter objects. Our objective in this paper is to provide a new approach to improve the accuracy and quality of the classification method based on the basic advantages of the Kohonen self-organization algorithm and on new network functions to pre-eliminate the auto-detected of drawbacks and redundancy.
关键词：thesai; IJACSA; thesai.org; journal; IJACSA papers; Artificial neural networks; self-organization map; Learning algorithm; Classification; Clustering; Principal components Analysis; power iteration
