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Content Provider | IEEE Xplore Digital Library |
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Author | Yen, G.G. Zheng Wu |
Copyright Year | 2005 |
Description | Author affiliation: Sch. of Electr. & Comput. Eng., Oklahoma State Univ., Stillwater, OK, USA (Yen, G.G.; Zheng Wu) |
Abstract | The self-organizing map (SOM) is an efficient tool for visualizing high-dimensional data as it performs a topology-presenting projection of the input space on a low-dimensional grid. To utilize the information provided by the SOM and obtain an approximation of the data structure, a separate data projection method is usually needed. However, most of the SOM projection methods are computationally expensive when the size of the data set becomes large. In this paper, we present an intuitive and effective SOM projection method with comparatively low computational complexity for the purpose of cluster visualization. This method maps data vectors on the output space based on their responses to different prototype vectors. High-resolution maps can be obtained with a relatively small network size. The proposed method is demonstrated using both an artificial and a real world data set. |
Starting Page | 1587 |
Ending Page | 1592 |
File Size | 3083469 |
Page Count | 6 |
File Format | |
ISBN | 0780390482 |
DOI | 10.1109/IJCNN.2005.1556115 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2005-07-31 |
Publisher Place | Canada |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Data visualization Self organizing feature maps Prototypes Neurons Data structures Computational complexity Intelligent systems Intelligent control Control systems Laboratories |
Content Type | Text |
Resource Type | Article |
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