臺灣博碩士論文加值系統

智識建構方法讓研究者瞭解學術領域議題的知識結構，透過當中的因素分析法，可以將學術領域議題分成若干個子議題，同時經由因素分析法產出皮爾森相關係數矩陣，能進一步繪製成智識結構圖，讓研究者得以視覺化方式瞭解知識結構。然而因素分析法假設分析議題資料為常態分配，因此忽略資料不符合常態分配的情況，以及因素分析法將領域議題分成若干個子議題時，是使用因素負荷量門檻值來過濾並將文章歸屬子議題，因此可能將重要資訊過濾掉，並且會有文章因素負荷量過低而無法判別歸屬哪個子議題的情況。而在使用皮爾森相關係數矩陣當作智識結構圖輸入時，皮爾森相關係數矩陣值域介於-1到1之間，因此會有負值相關係數轉換為正值距離問題。因此本研究使用期望值最大化演算法取代因素分析法，解決並改進上述現在通用之智識建構方法中的問題。
本研究利用自行開發的智識建構系統從微軟學術資料庫蒐集兩個議題，使用因素分析法產生出轉軸因素集群，同時擷取智識建構流程中產出共被引矩陣與因素負荷量矩陣，當作期望值最大化演算法輸入，產生出共被引EM集群與轉軸EM集群，而為了更加嚴謹評估智識建構方法分群結果，本研究亦使用文字探勘方法，以內文之餘弦相似度矩陣當作期望值最大化演算法輸入，產生出餘弦EM集群。最後透過相關的集群一致性方法(Jaccard Similarity、Rand Index、Kappa與Gwet’s)，以及Jensen Shannon Divergence (JSD)之內文一致性方法評估集群的分群結果，同時利用Quadratic Assignment Procedure (QAP)之智識網絡一致性方法評估集群的智識結構圖，最後發現使用共被引矩陣為基礎，經由期望值最大化演算法，產生出的共被引EM集群的集群分群結果有較高的內文一致性，以及共被引集群與因素集群的智識網絡一致性最高，因此共被引EM集群的智識結構圖有較佳的結果。

Intellectual Structure (IS) is a method that has been widely applied in knowledge domain analysis and science mapping. Factor analysis is often applied in the Intellectual Structure methods to reduce the dimension of the data by ascribing multiple documents to single factors. Each factor generally represents one research theme. The Pearson correlation coefficient matrix, which is derived from the factor analysis, is used to construct the intellectual structure diagram that facilitates knowledge domain visualization. However, factor analysis assumes the input data is normally distributed, which is an untested premise. An article may not be assigned to any factor due to its low factor loading. In order to amend the issues of untested normal distribution premise and unassigned articles, we replace the factor analysis method with Expectation Maximization to carry out several experiments and compare the results.

The results of our experiment provide empirical evidence that the EM-based intellectual structure method generates more coherent document clusters than the conventional one. With the supporting results on hand, we can improve the intellectual structure methodology by using Expectation Maximization to replace the conventionally used factor analysis.

誌 謝 I
中文論文提要 II
英文論文提要 III
目次 IV
表次 VII
圖次 IX
第一章、 緒論 1
第一節、 研究背景與動機 1
第二節、 研究目的 4
第三節、 論文架構 4
第二章、 文獻探討 6
第一節、 文獻關聯性的方法 6
2.1 引文分析 6
第二節、 集群方法 7
2.2.1 因素分析 8
2.2.1.1 探索性因素分析 8
2.2.1.2 因素萃取（Extraction of Factor） 9
2.2.1.3 因素轉軸（Factor Rotation） 9
2.2.1.4 因素分析的應用 11
2.2.2 多元尺度分析法 12
2.2.3 階層式群集分析法 13
2.2.4 K-means分群法 14
2.2.5 自我組織映射圖 15
2.2.6 期望值最大化演算法 16
第三節、 智識建構繪圖方法 19
2.3.1 徑路搜尋法 19
第四節、 不同智識建構方法的評估與比較 21
2.4.1 Rand Index 21
2.4.2 Jaccard Index 22
2.4.3 Kappa & Gwet’s AC1 22
2.4.4 Jensen-Shannon Divergence 24
2.4.5 Quadratic Assignment Procedure 24
2.4.6 評估方法小結 25
第三章、 研究方法 27
第一節、 研究架構 27
第二節、 資料處理 29
第三節、 集群分析 30
第四節、 智識繪圖 33
第五節、 智識建構方法評估比較 35
第四章、 研究結果 38
第一節、 研究實作 38
4.1.1 文獻蒐集 38
4.1.2 集群分析 41
4.1.3 視覺化分析 43
第二節、 研究評估 47
4.2.1 集群配對 47
4.2.2 集群一致性 51
4.2.3 內文一致性 54
4.2.4 智識網絡一致性 58
4.2.5 研究評估小結 59
4.2.6 議題解讀 60
4.2.7 智識建構圖解讀 66
第五章、 結論與建議 68
第一節、 研究貢獻 68
第二節、 研究限制 69
第三節、 未來研究建議 69
參考文獻 70
附錄一、NFC文獻集之文獻列表 74
附錄二、Citation Analysis文獻集之文獻列表 83
附錄三、Jaccard配對之系統操作流程 93
附錄四、Rand Index檢定之操作流程 94
附錄五、Kappa檢定之SPSS操作流程 95
附錄六、Gwet’s檢定之操作流程 97
附錄七、JSD檢定之系統操作流程 98
附錄八、QAP檢定之UCINET操作流程 100
附錄九、產生餘弦相似度矩陣之Rapid Miner操作流程 102
附錄十、EM演算法集群分析之MATLAB操作流程 104
簡　　歷 108
著作權聲明 109

中文參考文獻
吳萬億, & 林清河. (2000). 企業研究方法. 臺北市: 華泰文化.
林震岩. (2006). 多變量分析: SPSS 的操作與應用. 台北市: 智勝文化.
劉軍. (2009). 整體網分析講義: UCINET 軟件實用指南. 上海市: 格致.
黃馨儀. (2014，5月)。 近場通訊研究內涵與其相關研究領域。 論文發表於中華民國第二十五屆國際資訊管理學術研討會，臺中市國立中興大學資訊管理學系。
英文參考文獻
Boyack, K. W., & Klavans, R. (2010). Co-citation analysis, bibliographic coupling, and direct citation: Which citation approach represents the research front most accurately? Journal of the American Society for Information Science and Technology, 61(12), 2389-2404.
Brin, S., & Page, L. (1998). The anatomy of a large-scale hypertextual Web search engine. Computer Networks and ISDN Systems, 30(1–7), 107-117.
Carletta, J. (1996). Assessing agreement on classification tasks: the kappa statistic. Comput. Linguist., 22(2), 249-254.
Chen, C. (2006). Information Visualization: Beyond the Horizon: Springer-Verlag New York, Inc.
Chen, T. T. (2012). The development and empirical study of a literature review aiding system. Scientometrics, 92(1), 105-116. doi: 10.1007/s11192-012-0728-3
Chen, T. T. (2015). The congruity between linkage-based factors and content-based clusters—an experimental study using multiple document corpora. Journal of the Association for Information Science and Technology, n/a-n/a. doi: 10.1002/asi.23413
Clarke, K. R. (1993). Non-parametric multivariate analyses of changes in community structure. Australian Journal of Ecology, 18(1), 117-143.
Culnan, M. J. (1987). Mapping the Intellectual Structure of MIS, 1980-1985: A Co-Citation Analysis. MIS Quarterly, 11(3), 341-353.
Dearholt, D. W., & Schvaneveldt, R. W. (1990). Properties of pathfinder networks. In W. S. Roger (Ed.), Pathfinder associative networks (pp. 1-30): Ablex Publishing Corp.
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum Likelihood from Incomplete Data via the EM Algorithm. Journal of the Royal Statistical Society. Series B (Methodological), 39(1), 1-38.
Ding, Y., Chowdhury, G., & Foo, S. (1999). Mapping the intellectual structure of information retrieval studies: an author co-citation analysis, 1987-1997. Journal of information science, 25(1), 67-78.
Dittenbach, M., Merkl, D., & Rauber, A. (2000, 2000). The growing hierarchical self-organizing map. Paper presented at the Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on.
Feldman, R., Fresko, M., Kinar, Y., Lindell, Y., Liphstat, O., Rajman, M., . . . Zamir, O. (1998). Text mining at the term level. In J. Żytkow & M. Quafafou (Eds.), Principles of Data Mining and Knowledge Discovery (Vol. 1510, pp. 65-73): Springer Berlin Heidelberg.
Futschik, M., & Carlisle, B. (2005). Noise-Robust Soft Clustering of Gene Expression Time-Course Data. Journal of Bioinformatics and Computational Biology, 03(04), 965-988. doi: doi:10.1142/S0219720005001375
Garfield, E. (1955). Citation indexes for science; a new dimension in documentation through association of ideas. Science (New York, N.Y.), 122(3159), 108-111.
Garfield, E., Malin, M. V., & Small, H. (1983). Citation data as science indicators. In: Y Elkana (ed.), Toward a Metric of Science. New York: Wiley.
Goldsmith, T. E., Johnson, P. J., & Acton, W. H. (1991). Assessing structural knowledge. Journal of educational psychology, 83(1), 88.
Gwet, K. (2002). Inter-rater reliability: dependency on trait prevalence and marginal homogeneity. Statistical Methods for Inter-Rater Reliability Assessment Series, 2, 1-9.
Härdle, W., & Simar, L. (2007). Applied multivariate statistical analysis (Vol. 22007): Springer.
Harman, H. H. (1976). Modern factor analysis: University of Chicago Press.
Hendrickson, A. E., & White, P. O. (1964). PROMAX: A QUICK METHOD FOR ROTATION TO OBLIQUE SIMPLE STRUCTURE. British Journal of Statistical Psychology, 17(1), 65-70.
Hsiao, C. H., & Yang, C. (2011). The intellectual development of the technology acceptance model: A co-citation analysis. International Journal of Information Management, 31(2), 128-136.
Hubert, L., & Arabie, P. (1985). Comparing partitions. Journal of Classification, 2(1), 193-218.
Jaccard, P. (1901). Distribution de la Flore Alpine: dans le Bassin des dranses et dans quelques régions voisines: Rouge.
Jiawei, H., & Kamber, M. (2001). Data mining: concepts and techniques. San Francisco, CA, itd: Morgan Kaufmann, 5.
Kaiser, H. (1958). The varimax criterion for analytic rotation in factor analysis. Psychometrika, 23(3), 187-200.
Kangas, J., & Kohonen, T. (1996). Developments and applications of the self-organizing map and related algorithms. Mathematics and Computers in Simulation, 41(1–2), 3-12.
Kohonen, T. (1982). Self-organized formation of topologically correct feature maps. Biological Cybernetics, 43(1), 59-69.
Kohonen, T. (1998). The self-organizing map. Neurocomputing, 21(1–3), 1-6.
Kotsiantis, S., & Pintelas, P. (2004). Recent advances in clustering: A brief survey. WSEAS Transactions on Information Science and Applications, 1(1), 73-81.
Macqueen, J. (1967). Some methods for classification and analysis of multivariate observations. Paper presented at the Proc. 5th Berkeley Symp. Mathematical Statist. Probability.
McLachlan, G., & Krishnan, T. (1997). The EM Algorighm and Extensions: John Wiley and Sons.
Meilă, M., & Heckerman, D. (2001). An Experimental Comparison of Model-Based Clustering Methods. Machine Learning, 42(1-2), 9-29.
Moon, T. K. (1996). The expectation-maximization algorithm. Signal Processing Magazine, IEEE, 13(6), 47-60.
Moya-Anegón, F., Herrero-Solana, V., & Jiménez-Contreras, E. (2006). A connectionist and multivariate approach to science maps: the SOM, clustering and MDS applied to library and information science research. Journal of information science, 32(1), 63-77.
Neuhaus, J. O., & Wrigley, C. (1954). THE QUARTIMAX METHOD. British Journal of Statistical Psychology, 7(2), 81-91.
Norris, M., & Lecavalier, L. (2010). Evaluating the Use of Exploratory Factor Analysis in Developmental Disability Psychological Research. Journal of Autism and Developmental Disorders, 40(1), 8-20. doi: 10.1007/s10803-009-0816-2
Park, H. S., Dailey, R., & Lemus, D. (2002). The Use of Exploratory Factor Analysis and Principal Components Analysis in Communication Research. Human Communication Research, 28(4), 562-577. doi: 10.1111/j.1468-2958.2002.tb00824.x
Punj, G., & Stewart, D. W. (1983). Cluster Analysis in Marketing Research: Review and Suggestions for Application. Journal of Marketing Research, 20(2), 134-148.
Santos, J., & Embrechts, M. (2009). On the Use of the Adjusted Rand Index as a Metric for Evaluating Supervised Classification. In C. Alippi, M. Polycarpou, C. Panayiotou & G. Ellinas (Eds.), Artificial Neural Networks – ICANN 2009 (Vol. 5769, pp. 175-184): Springer Berlin Heidelberg.
Saunders, D. R. (1961). The rationale for an “oblimax” method of transformation in factor analysis. Psychometrika, 26(3), 317-324.
Sickle, J. V. (1997). Using Mean Similarity Dendrograms to Evaluate Classifications. Journal of Agricultural, Biological, and Environmental Statistics, 2(4), 370-388.
Spearman, C. (1904). "General Intelligence," Objectively Determined and Measured. The American Journal of Psychology, 15(2), 201-292. doi: 10.2307/1412107
Torgerson, W. (1952). Multidimensional scaling: I. Theory and method. Psychometrika, 17(4), 401-419.
Wongpakaran, N., Wongpakaran, T., Wedding, D., & Gwet, K. L. (2013). A comparison of Cohen’s Kappa and Gwet’s AC1 when calculating inter-rater reliability coefficients: a study conducted with personality disorder samples. BMC medical research methodology, 13(1), 61.
Yen, G. G., & Zheng, W. (2006). A Self-Organizing Map Based Approach for Document Clustering and Visualization. Paper presented at the Neural Networks, 2006. IJCNN '06. International Joint Conference on.
Yen, G. G., & Zheng, W. (2008). Ranked Centroid Projection: A Data Visualization Approach With Self-Organizing Maps. Neural Networks, IEEE Transactions on, 19(2), 245-259.