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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 翁昭旼(Jau-Min Wong) | |
dc.contributor.author | Po-Chun Huang | en |
dc.contributor.author | 黃柏鈞 | zh_TW |
dc.date.accessioned | 2021-06-13T00:13:38Z | - |
dc.date.available | 2009-07-31 | |
dc.date.copyright | 2007-07-31 | |
dc.date.issued | 2007 | |
dc.date.submitted | 2007-07-26 | |
dc.identifier.citation | 【1】 Data Mining A Tutorial-based Primer Richard J. Roiger, Michael W. Geatz, 2003
【2】 Martin, J.E., Handbook on Knowledge Management,Springer,2002 【3】 McAleese, R., “A theoretical view on concept mapping”, Association for Learning Technology Journal, Vol. 2, No. 1, pp. 38–48, 1994 【4】 Schultze L. & Leidner, D.E., “Studying Knowledge Management in Information System Research: Discourses and Theoretical Assumptions”, MIS Quarterly, Vol.26, No. 3, 2002 【5】 Hsiang-Chun Tsai, Web-base Literature Clustering Search,2005 【6】 Medern Information Retrieval 【7】 Text information retrieval system 【8】 Entrez PubMed,http://www.ncbi.nlm.nih.gov/entrez 【9】 Information Storage and Retrieval Systems Theory and Implementation.Gerald J.Kowalski.Mark T. Maybury 【10】 M.A.Hearst and A.S. Schwartz. A simple algorithm for identifying abbreviation definitions in biomedical text. Proceedings of the Pacific Symposium on Biocomputing,2003. 【11】 I. Dere、nyi,G. Palla, T. Vicsek, Clique Percolation in Random Networks. Physical Review Letters, Vol. 94. Issue 16, 2005. 【12】 B. Adamcsek, Uncovering the overlapping community structure of complex networks in nature and society. Nature, 435, 814-818, 2005 【13】 Statistic : Robert S. Witte , John S. Witte 1996 【14】 Finding Groups in Data: An Introduction to Cluster Analysis.John Wiley & Sons Ltd, Chinchester ,New York, Weinheim. 【15】 H(o)ppner F,Klawonn F,Kruse R.Fuzzy Cluster Analysis:Methods for Classification,Data Analysis,and Image Recognition.New York:Wiley,1999 【16】 A. K. Jain , M. N. Murty , P. J. Flynn, Data clustering: a review, ACM Computing Surveys (CSUR), v.31 n.3, p.264-323, Sept. 1999 【17】 J. B. MacQueen: 'Some Methods for classification and Analysis of Multivariate Observations', Proceedings of 5-th Berkeley Symposium on Mathematical Statistics and Probability, Berkeley, University of California Press, 1:281-297,1967 【18】 Fast algorithm for detecting community structure in networks. Phys. Rev. E 69, 066133 ,2004. 【19】 LandmarL. Guibas Q. Fang, J. Gao, V. de Silva, and L. Zhang. GLIDER: Gradient landmark-based distributed routing for sensor networks. In 24rd Conf. of the IEEE Communications Society (INFOCOM), 2005. 【20】 B. Sergey, P. Lawrence, The Anatomy of a Large-Scale Hypertextual Web Search Engine. Computer Networks and ISDN Systems, 30(1-7),107-117, 1998 【21】 C. Ding, X. He, P. Husbands, H. Zha, H. Simon, PageRank, HITS and a unified framework for link analysis. ACM Press, 353-354, 2002. 【22】 G.W. Flake, S. Lawrence, C.L. Giles, F.M. Coetzee, Self-organization and identification of Web communities. IEEE, Volume 35, Issue 3, 66-70, 2002. 【23】 Kart00,http://www.kartoo.com 【24】 H. Liu, Z.Z. Hu, J.Z. Wu, C. Wu, Biothesaurus:a web-based thesaurus of protein and gene names. BMC Bioinformatics, Vol. 22, No. 1,103-105,2006. 【25】 D. Ian, M. Joel, PreBIND and Textomy – mining the biomedical literature for protein-protein interactions using a support vector machine. BMC Bioinformatics, 4-11, 2003. 【26】 J.J. Lars, J. Saric , P. Bork, Literatue mining for the biologist:from information retrieval to bioblogical discovery. Nature Review Genetics 7,119-129, 2006. 【27】 B. Fritzke. Growing cell structures - a self- organizing network for unsupervised and supervised learning, Neural Networks, vol. 7, no. 9, pp. 1441- 1460, 1994. 【28】 H. Hu, X. Yan, Y. Huang, J. Han, and X. Jasmine Zhou. Mining coherent dense subgraphs acrossmassive biological networks for functional discovery. In Proc. of 2005 Int. Conf. on IntelligentSystems for Molecular Biology (ISMB’05), pages 213–221, 2005. 【29】 Building and Applying a Concept Hierarchy Representation of a User Profile. 【30】 http://nlp.stanford.edu/ | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/28599 | - |
dc.description.abstract | 隨著科技的進步與網際網路的普及化,人們面臨一個新世界的到來,那就是:一個沒有時間空間隔閡的資訊世界。在這個資訊世界中,藉由網際網路的便利性,使用者可以順速且方便的獲取想得到的資訊。但另一方面,也因資訊過度的累積膨脹,造成了資訊負載的情況,造成使用者在面對過多資訊時,無法有效的掌握想得到的資訊造成勞力時間上的負擔。因此,如何有效的掌握資訊的重點是一個值得思考解決的問題。
本研究主要目的為:提供使用者一個知識呈現系統,並藉由知識領域中所包含的特徵,以及知識領域中特徵與特徵間的關係,來加以釐清知識領域與知識領域的關係,並且探討哪些特徵是連接知識領域與知識領域的溝通橋樑,哪些特徵是該知識領域獨特的特徵。在本篇論文中,使用了三種不同知識領域分別為Inflammatory Bowel Disease、Irritable Bowel Syndrome、Brain Tumor用來表達知識領域關係的遠近,並以chi-square test量測知識與知識的分離與否。 | zh_TW |
dc.description.abstract | With the development of technology and popularity of internet, modern people are in approaching of brand-new world:A world provides information without time and distance gap. Under the circumstances, users can easily grab desirable information through Internet. But on the contrary, It is likely to bring out a problem of over-downloading and make more burden in time and human resource when dealing with too much invaluable information. Therefore, it is a worthy issue to consider how to efficiently obtain valuable information.
The main purpose of this study is:Providing a knowledge representation system and distinguishing the relationships from knowledge by identifying features of knowledge domain and their relationships. Moreover, we discuss what features connect with variety of knowledge and which are unique ones. In this research, We using three different methods of knowledge domain such as Inflammatory Bowel Disease, Irritable Bowel Syndrome and Brain Tumor to express the distance of knowledge relationship and access whether knowledge split by chi-square test. | en |
dc.description.provenance | Made available in DSpace on 2021-06-13T00:13:38Z (GMT). No. of bitstreams: 1 ntu-96-R94548058-1.pdf: 1848423 bytes, checksum: be7ae0ea1133637f107c02526ddde936 (MD5) Previous issue date: 2007 | en |
dc.description.tableofcontents | 目錄
誌謝 Ⅰ 中文摘要 Ⅱ 英文摘要 Ⅲ 目錄 Ⅳ 圖目錄 Ⅵ 表目錄 Ⅸ 第一章 緒論 1 1.1研究背景與動機 1 1.2研究目的 2 1.3論文架構 3 第二章 相關研究 4 2.1網頁文獻叢集化搜尋 4 2.2 詞性標示(Part Of Speech Tagging) 5 2.3 資料分群 5 2.4 網路分析 7 第三章 研究材料 8 3.1 PubMed 8 3.2 MEDLINE 8 3.2.1 MEDLINE常用的縮寫字 9 3.3 MEDLINE資料(研究初步資料) 9 第四章 研究方法 10 4.1 系統流程架構圖 11 4.2 醫學文獻前處理 11 4.3 醫學文獻關鍵字片語選取 12 4.4 醫學文獻關鍵字片語權重計算與選取 14 4.5 醫學文獻關聯法則 15 4.5.1 Support 15 4.5.2 Confidence 16 4.6 醫學文獻分群 16 4.7 醫學文獻關鍵字色彩分配 20 4.8 Domain Distribution threshold定義 20 4.9 Term Association threshold定義 21 第五章 知識呈現系統 23 第六章 評估與討論 25 6.1 評估結果 25 第七章 結論 49 第八章 未來展望 51 參考文獻 52 附錄 55 圖目錄 圖(1)資料起始圖 6 圖(2)階層式分群法結果 6 圖(3)System Architecture 11 圖(4)MEDLINE 12 圖(5)K3~ K5完全圖 17 圖(6)K5中所包含K3與K4的完全子圖 18 圖(7)Clique Percolation Method示意圖 19 圖(8)系統介面 23 圖(9)三大知識視覺化呈現 24 圖(10)4-clique時視覺化呈現 24 圖(11)3-clique下調整domain distribution bar三種關係的 chi square值分布圖 26 圖(12)4-clique下調整domain distribution bar三種關係的 chi square值分布圖 27 圖(13)5-clique下調整domain distribution bar三種關係的 chi square值分布圖 27 圖(14)3-clique domain distribution IBD-IBS 27 圖(15)3-clique domain distribution IBD-Brain 28 圖(16)3-clique domain distribution IBS-Brain 29 圖(17)3-clique下調整Association Threshold bar三種關係的 chi square值分布圖 30 圖(18)4-clique下調整Association Threshold bar三種關係的 chi square值分布圖 31 圖(19)5-clique下調整Association Threshold bar三種關係的 chi square值分布圖 31 圖(20)3-clique association IBD-IBS 32 圖(21)3-clique association IBD-Brain 33 圖(22)3-clique association IBS-Brain 34 圖(23)3-clique IBD unique term 34 圖(24)3-clique IBS unique term 35 圖(25)3-clique IBD unique term 35 圖(26)3-clique Brain unique term 36 圖(27)3-clique IBS unique term 36 圖(28)3-clique Brain unique term 37 圖(29)4-clique IBD unique term 37 圖(30)4-clique IBS unique term 38 圖(31)4-clique IBD unique term 39 圖(32)4-clique Brain unique term 39 圖(33)4-clique IBS unique term 40 圖(34)4-clique Brain unique term 40 圖(35)not in 3-clique IBD unique term 41 圖(36)not in 3-clique IBS unique term 41 圖(37)not in 3-clique IBD unique term 42 圖(38)not in 3-clique Brain unique term 43 圖(39)not in 3-clique IBS unique term 43 圖(40)not in 3-clique Brain unique term 44 圖(41)not in 4-clique IBD unique term 45 圖(42)not in 4-clique IBS unique term 45 圖(43)not in 4-clique IBD unique term 46 圖(44)not in 4-clique Brain unique term 47 圖(45)not in 4-clique IBS unique term 47 圖(46)not in 4-clique Brain unique term 48 表目錄 表(1)片語樣式 13 表(2)顏色分配表 20 表(3)Domain Distribution threshold參照表格 21 表(4)Association Rule表格 22 表(5)3-clique下調整domain distribution bar三種關係的chi square值 評估 25 表(6)4-clique下調整domain distribution bar三種關係的chi square值 評估 25 表(7)5-clique下調整domain distribution bar三種關係的chi square值 評估 26 表(8)3-clique下調整Association Threshold bar三種關係的chi square值 評估 29 表(9)4-clique下調整Association Threshold bar三種關係的chi square值 評估 29 表(10)5-clique下調整Association Threshold bar三種關係的chi square值 評估 30 | |
dc.language.iso | zh-TW | |
dc.title | 分群關係知識呈現系統 | zh_TW |
dc.title | Knowledge Representation System by Cluster Relation | en |
dc.type | Thesis | |
dc.date.schoolyear | 95-2 | |
dc.description.degree | 碩士 | |
dc.contributor.coadvisor | 蔣以仁(I-Jen Chiang) | |
dc.contributor.oralexamcommittee | 陳中明(Chung-Ming Chen) | |
dc.subject.keyword | 知識呈現,群集分析,文字探勘, | zh_TW |
dc.subject.keyword | knowledge representation,cluster analysis,text mining, | en |
dc.relation.page | 65 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2007-07-28 | |
dc.contributor.author-college | 工學院 | zh_TW |
dc.contributor.author-dept | 醫學工程學研究所 | zh_TW |
顯示於系所單位: | 醫學工程學研究所 |
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