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Title:
IdeaGraph plus: A Topic-Based Algorithm for Perceiving Unnoticed Events
Author: Zhang, Chen ; Wang, Hao ; Xu, Fanjiang ; Hu, Xiaohui
Conference Name: IEEE 13th International Conference on Data Mining (ICDM)
Conference Date: DEC 07-10, 2013
Issued Date: 2013
Conference Place: Dallas, TX
Keyword: Chance Discovery ; Knowledge Discovery ; Topic Model ; Idea Graph plus ; Latent Information
Publish Place: IEEE
Indexed Type: CPCI
ISSN: 1550-4786
ISBN: 978-0-7695-5109-8
Department: [Zhang, Chen; Wang, Hao; Xu, Fanjiang; Hu, Xiaohui] Chinese Acad Sci, State Key Lab Comp Sci, Inst Software, Beijing 100190, Peoples R China.
Abstract: In the last few years, chance discovery as an extension of data mining has been proposed to capture rare but significant chances from a single document data for human decision making. Key Graph is a useful miner algorithm as well as a tool to discover chance candidates. On base of that, Idea Graph extended the concept of a chance to uncover more valuable chances. However, Key Graph and Idea Graph both fail to consider semantic relations among terms. In this paper, we propose an improved algorithm called Idea Graph plus which makes use of semantic information to enhance the performance of scenario construction using LDA topic model. Additionally, the term overlaps between sub-scenarios provide a thinking space for human to perceive unnoticed chances. An experiment demonstrates the superiority of Idea Graph plus by comparing with IdeaGraph.
English Abstract: In the last few years, chance discovery as an extension of data mining has been proposed to capture rare but significant chances from a single document data for human decision making. Key Graph is a useful miner algorithm as well as a tool to discover chance candidates. On base of that, Idea Graph extended the concept of a chance to uncover more valuable chances. However, Key Graph and Idea Graph both fail to consider semantic relations among terms. In this paper, we propose an improved algorithm called Idea Graph plus which makes use of semantic information to enhance the performance of scenario construction using LDA topic model. Additionally, the term overlaps between sub-scenarios provide a thinking space for human to perceive unnoticed chances. An experiment demonstrates the superiority of Idea Graph plus by comparing with IdeaGraph.
Language: 英语
Content Type: 会议论文
URI: http://ir.iscas.ac.cn/handle/311060/16504
Appears in Collections:软件所图书馆_会议论文

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Recommended Citation:
Zhang, Chen,Wang, Hao,Xu, Fanjiang,et al. IdeaGraph plus: A Topic-Based Algorithm for Perceiving Unnoticed Events[C]. 见:IEEE 13th International Conference on Data Mining (ICDM). Dallas, TX. DEC 07-10, 2013.
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