NDLI: Ranked Centroid Projection: A Data Visualization Approach With Self-Organizing Maps

Content Provider	IEEE Xplore Digital Library
Author	Yen, G.G. Zheng Wu
Copyright Year	1990
Abstract	The self-organizing map (SOM) is an efficient tool for visualizing high-dimensional data. In this paper, the clustering and visualization capabilities of the SOM, especially in the analysis of textual data, i.e., document collections, are reviewed and further developed. A novel clustering and visualization approach based on the SOM is proposed for the task of text mining. The proposed approach first transforms the document space into a multidimensional vector space by means of document encoding. Afterwards, a growing hierarchical SOM (GHSOM) is trained and used as a baseline structure to automatically produce maps with various levels of detail. Following the GHSOM training, the new projection method, namely the ranked centroid projection (RCP), is applied to project the input vectors to a hierarchy of 2D output maps. The RCP is used as a data analysis tool as well as a direct interface to the data. In a set of simulations, the proposed approach is applied to an illustrative data set and two real-world scientific document collections to demonstrate its applicability.
Sponsorship	IEEE Computational Intelligence Society
Page Count	15
File Size	1933697
Starting Page	245
Ending Page	259
File Format	PDF
ISSN	10459227
Volume Number	19
Issue Number	2
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-02-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Data visualization Self organizing feature maps Multidimensional systems Data analysis Data mining Clustering algorithms Principal component analysis Text mining Encoding Production self-organizing map (SOM) document clustering
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Networks and Communications Computer Science Applications Software

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