NDLI: Using a Combination Method of MDS and SOM to Visually Analyze Postpartum Depression Domain

Content Provider	IEEE Xplore Digital Library
Author	Xi Meng Ruifang Shen Jianqiang Li Jijiang Yang
Copyright Year	2015
Description	Author affiliation: People' Public Security Univ. of China, Beijing, China (Xi Meng) \|\| Tsinghua Nat. Labs. for Inf. Sci. & Technol., Tsinghua Univ., Beijing, China (Ruifang Shen; Jijiang Yang) \|\| Sch. of Software Eng., Beijing Univ. of Technol., Beijing, China (Jianqiang Li)
Abstract	This study focused on a combination method that included MDS and SOM to visually reveal research topics in postpartum depression research domain from the bibliographic point of view. 391 documents which were under postpartum depression domain were used to be analyzed in this study. Considering that MDS and SOM each dimension reduction method has its own disadvantages, we used the two visualization techniques. The nonmetric MDS method (Sammon's mapping) combined with self-organizing map was employed to separate documents based on their topics, and a cluster analysis was conducted. The experiment result proved that Sammon's mapping combined with self-organizing map techniques yielded the better visualization effect in revealing postpartum depression domain.
Starting Page	95
Ending Page	95
File Size	390486
Page Count	1
File Format	PDF
ISSN	07303157
e-ISBN	9781467365642
DOI	10.1109/COMPSAC.2015.226
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-07-01
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Data visualization Pediatrics XML Text mining Visualization Artificial neural networks Web mining multidimensional scaling (MDS); Sammons mapping; self-organizing map(SOM); postpartum depression (PPD); visual analysis; visualization method
Content Type	Text
Resource Type	Article
Subject	Computer Science Applications Software

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