NDLI: Extended Kernel Self-Organizing Map Clustering Algorithm

Content Provider	IEEE Xplore Digital Library
Author	Ning Chen Hongyi Zhang
Copyright Year	2009
Abstract	The self-Organizing Map allows to visualize the underlying structure of high dimensional data. However, the original relies on the use of Euclidean distances which often becomes a serious drawback for number of real problems. Donald and others map the data in input space into a high 2-dimension feature space, here SOM algorithm are performed. However, its disadvantage lies in lack of direct descriptions about the clustering’s center and result .In this paper, we extend of SOM, a novel kernel SOM algorithm is proposed from energy function. The idea of kernel Self-Organizing Map is applied to kernel trick. The inner product of the mapping value of the original data in feature space is replaced by a kernel function, the winner neuron and weights of each neuron can be initialized and updated by kernel Euclidean norm in the feature space. This trick resolve the non-liners can’t clustering in the input space and can’t direct descriptions about the clustering’s center and result. In this paper, some data are applied to test KSOM and SOM algorithm,The result of the experiments show KSOM algorithm has better performance than SOM.
Starting Page	454
Ending Page	458
File Size	950803
Page Count	5
File Format	PDF
ISBN	9780769537368
DOI	10.1109/ICNC.2009.682
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-08-14
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis clustering algorithm feature space Neurons Educational institutions kernel function Mechanical engineering energy function Space technology Clustering algorithms Data visualization self-organizing map Kernel Testing Power engineering and energy
Content Type	Text
Resource Type	Article

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