參考文獻
一、中文部分
1.尹相志(2007),SQL Server 2005 Data Mining資料採礦與Office 2007資料採礦增益集,臺北:悅知文化。
2.方鄒昭聰 & 楊迪強(2008),運用K-Means演算法探討人體動作因子之組成-以LMA為基礎,2008管理創新與新願景研討會論文集,臺北。
3.吳啟聰譯(2002),商用統計學入門與應用 (第 1 版),臺北:麥格羅希爾。
4.李佳宜、林克忠、吳菁宜、連倚南 & 許美慧(2006),改良式侷限誘發動作治療於輕中度腦中風患者之成效:運動學分析之研究,職能治療學會雜誌,24,頁 25-35。5.國立臺北藝術大學舞蹈學院動作創意實驗室(2006),經濟部學界開發產業技術計畫執行報告-人體動作質地分析與肢體情緒數位傳達應用開發三年計畫第一年度執行成果報告書,計畫編號:95-EC-17-A-02-S1-052,臺北。
6.康琳茹、陳祐蘋、宋文旭、莊天祐、李淑貞、蔡美文等(2005),虛擬實境對腦性麻痺兒童伸取行為之訓練療效:個案報告,物理治療,30 (6),頁 339-347。
7.張中煖(2002),現代舞教學源流初探,藝術評論 (13),頁 273-284。8.陳五洲 & 黃彥慈(2007),拉邦動作分析論,大專體育 (88),頁 169-175。9.曾龍譯(2003),資料探礦概念與技術 (第 1 版),臺北:維科。
10.經濟部工業局(2007),2007數位內容產業年鑑 (第 1 版),臺北:經濟部。
11.羅孟剛(2008),資料採礦應用於人體動作質地分析-以LMA為基礎,國立臺北大學資訊管理研究所碩士論文,未出版,臺北。 二、英文部分
1.Badler, I. N., Chi, D. M., & Chopra, S. (1999). Virtual human animation based on movement observation and cognitive behavior models. Proceedings of the Computer Animation Conference, (pp. 128-137).
2.Badler, N. I., & Smoliar, S. W. (1979). Digital Representations of Human Movement. ACM Computing Surveys , 1 (11), pp. 19-38.
3.Bradley, P. S., & Fayyad, U. M. (1998). Refining Initial Points for K-Means Clustering. Proceedings of the 15th International Conference on Machine Learning, (pp. 91-99).
4.Callennec, B. L., & Boulic, R. Robust Kinematic Constraint Detection for Motion Data. Proceedings of the 2006 ACM SIGGRAPH/Eurograp Symposium on Computer Animation, (pp. 281-290).
5.Chi, D. M., Costa, M., Zhao, L., & Badler, N. The EMOTE Model for Effort and Shape. Proceedings of the 27th annual conference on computer graphics and interactive techniques, (pp. 173-182).
6.Fangtsou, C. T., & Li, T. H. (2008). Body Movement Acquisition and Construct Data Exchange Protocol Based on Body Movement Quality Analysis. Proceedings of the e-CASE 2008 International Joint Conference on Advances in e-Commerce.
7.Fangtsou, C. T., & Yang, T. C. (2008). About Body Movement Factors by Using K-Means Algorithm. Proceedings of the e-CASE 2008 International Joint Conference on Advances in e-Commerce.
8.Han, J., & Kamber, M. (2001). Data Mining: Concepts and Techniques. San Francisco: Morgan Kaufmann.
9.Ieronutti, L., & Chittaro, L. (2005). A Virtual Human Architecture that Integrates Kinematic, Physical and Behavioral Aspects to Control H-Anim Characters. Proceedings of the tenth international conference on 3D Web technology, (pp. 39-48).
10.Nagamatsu, T., Kamahara, J., Iko, T., & Tanaka, N. (2008). One-point calibration gaze tracking based on eyeball kinematics using stereo cameras. Proceedings of the 2008 symposium on Eye tracking research & applications, (pp. 95-98).
11.Pelleg, D., & Moore, A. W. (2000). X-Means: Extending K-Means with Efficient Estimation of the Number of Clusters. Proceedings of the 17th International Conference on Machine Learning, (pp. 727-734).
12.Ray, S., & Turi, R. H. (1999). Determination of number of clusters in k-means clustering and application in colour image segmentation. Proceedings of the 4th International Conference on Advances in Pattern Recognition and Digital Techniques (ICAPRDT'99), (pp. 137-143).
三、網站資料
1.Nakata, T. (2002, Nov. 7). motionprint.html. Retrieved Oct. 13, 2007, from Motion Print: http://staff.aist.go.jp/toru-nakata/motionprint.html#English