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Ranked Centroid Projection: A data ...
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Wu, Zheng.
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Ranked Centroid Projection: A data visualization approach based on self-organizing maps.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Ranked Centroid Projection: A data visualization approach based on self-organizing maps./
作者:
Wu, Zheng.
面頁冊數:
133 p.
附註:
Source: Dissertation Abstracts International, Volume: 67-06, Section: B, page: 3363.
Contained By:
Dissertation Abstracts International67-06B.
標題:
Engineering, Electronics and Electrical. -
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3222064
ISBN:
9780542730672
Ranked Centroid Projection: A data visualization approach based on self-organizing maps.
Wu, Zheng.
Ranked Centroid Projection: A data visualization approach based on self-organizing maps.
- 133 p.
Source: Dissertation Abstracts International, Volume: 67-06, Section: B, page: 3363.
Thesis (Ph.D.)--Oklahoma State University, 2006.
The Self-Organizing Map (SOM) is an unsupervised neural network model that provides topology-preserving mapping from high-dimensional input spaces onto a commonly two-dimensional output space. In this study, 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 data mining. The proposed approach first transforms the document space into a multi-dimensional vector space by means of document encoding. Then a growing hierarchical SOM (GHSOM) is trained and used as a baseline framework, which automatically produces maps with various levels of details. Following the training of the GHSOM, a novel projection method, namely the Ranked Centroid Projection (RCP), is applied to project the input vectors onto a hierarchy of two-dimensional output maps. The projection of the input vectors is treated as a vector interpolation into a two-dimensional regular map grid. A ranking scheme is introduced to select the nearest R units around the input vector in the original data space, the positions of which will be taken into account in computing the projection coordinates.
ISBN: 9780542730672Subjects--Topical Terms:
626636
Engineering, Electronics and Electrical.
Ranked Centroid Projection: A data visualization approach based on self-organizing maps.
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The Self-Organizing Map (SOM) is an unsupervised neural network model that provides topology-preserving mapping from high-dimensional input spaces onto a commonly two-dimensional output space. In this study, 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 data mining. The proposed approach first transforms the document space into a multi-dimensional vector space by means of document encoding. Then a growing hierarchical SOM (GHSOM) is trained and used as a baseline framework, which automatically produces maps with various levels of details. Following the training of the GHSOM, a novel projection method, namely the Ranked Centroid Projection (RCP), is applied to project the input vectors onto a hierarchy of two-dimensional output maps. The projection of the input vectors is treated as a vector interpolation into a two-dimensional regular map grid. A ranking scheme is introduced to select the nearest R units around the input vector in the original data space, the positions of which will be taken into account in computing the projection coordinates.
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The proposed approach can be used both as a data analysis tool and as a direct interface to the data. Its applicability has been demonstrated in this study using an illustrative data set and two real-world document clustering tasks, i.e. the SOM paper collection and the Anthrax paper collection. Based on the proposed approach, a software toolbox is designed for analyzing and visualizing document collections, which provides a user-friendly interface and several exploration and analysis functions.
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The presented SOM-based approach incorporates several unique features, such as the adaptive structure, the hierarchical training, the automatic parameter adjustment and the incremental clustering. Its advantages include the ability to convey a large amount of information in a limited space with comparatively low computation load, the potential to reveal conceptual relationships among documents, and the facilitation of perceptual inferences on both inter-cluster and within-cluster relationships. (Copies available exclusively from MIT Libraries, Rm. 14-0551, Cambridge, MA 02139-4307. Ph. 617-253-5668; Fax 617-253-1690.)
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