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姓名 陳信伊(Hsin-Yi Chen)  查詢紙本館藏   畢業系所 資訊工程學系
論文名稱 星狀座標之軸排列於群聚視覺化之應用
(Axes Arrangement in Star Coordinates for Numerical Data Visualization)
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摘要(中) 視覺化方法使用圖形來表達資料所包含的資訊,因為以圖解的方式比資料本身更能讓人一目了然。Star Coordinates是一種以座標軸為基礎的多維度資料視覺化方法,將每一筆資料投影到二維平面上的一個點,讓使用者在資料探勘的初期得到資料的概觀。本篇論文提出一種自動化座標軸排列方法應用於Star Coordinates,擷取多維度資料中各屬性的相關性,利用遺傳演算法計算出一組最佳化之座標軸排列方式,藉此調整Star Coordinates中座標軸的排列順序與夾角,增強資料的群聚現象以改善Star Coordinates視覺化的結果,並提供自動播放工具,呈現一系列經過座標軸排列後的圖形,使用者可以在觀看圖形的過程中獲得資料隱藏的資訊。透過自動化的座標軸排列,使用者可以省略複雜的座標軸操作,並藉由視覺化圖形分析多個維度之間的共同關係,觀看資料之間群聚的趨勢,並檢視資料分佈中的異常狀況,掌握資料的主要特徵。
關鍵字(中) ★ 資訊視覺化
★ 多維度
★ 星狀座標
★ 遺傳演算法
關鍵字(英) ★ Information Visualization
★ Multidimension
★ Star Coordinates
★ Genetic Algorithm
論文目次 第1章 緒論.....................................................................................................................................- 1 -
1.1 本篇論文的貢獻........................................................................................................................- 3 -
1.2 論文架構...................................................................................................................................- 4 -
第2章 相關研究..............................................................................................................................- 5 -
2.1 降低維度的方法........................................................................................................................- 6 -
2.2 高維度資料視覺化方法............................................................................................................- 9 -
2.3 比較與討論..............................................................................................................................- 18 -
第3章 系統架構............................................................................................................................- 20 -
3.1 座標軸排列(AXES ARRANGEMENT)...................................................................................- 21 -
3.1.1 遺傳演算法(Genetic Algorithm)簡介.............................................................- 22 -
3.1.2 編碼(Encoding)...............................................................................................- 24 -
3.1.3 適應值的評估(Fitness evaluation)..................................................................- 25 -
3.1.4 交配與突變運算...................................................................................................- 29 -
3.2 自動播放(AUTO PLAY).......................................................................................................- 32 -
第4章 實驗結果與討論................................................................................................................- 34 -
4.1 座標軸排列的實驗..................................................................................................................- 35 -
4.2 評估方法.................................................................................................................................- 41 -
4.3 執行時間的評估......................................................................................................................- 42 -
4.4 自動播放.................................................................................................................................- 43 -
4.5 實驗討論.................................................................................................................................- 46 -
第5章 結論...................................................................................................................................- 47 -
參考文獻..............................................................................................................................................- 49 -
參考文獻 [1]. E. Kandogan: “Visualizing Multi-dimensional Clusters, Trends, and Outliers using Star Coordinates”, In Proc. of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, pages 107-116, 2001.
[2]. G.. Dunn and B. Everitt: “An Introduction to Mathematical Taxonomy”, Cambridge University Press Cambridge, MA, 1982.
[3]. H. H. Harman: “Modern Factor Analysis”, University of Chicago Press, 1967.
[4]. R. N. Shepard, A. K. Romney, and S. B. Nerlove: “Multidimensional Scaling”, Seminar Press New York, 1972.
[5]. C. Faloutsos and K. Lin: “Fastmap: A fast Algorithm for Indexing, Data-Mining and Visualization of Traditional and Multimedia Datasets”, In Proc. of ACM SIGMOD Int. Conf. on Management of Data, 1995.
[6]. M. Ankerst: “Visual Data Mining”, Ludwig Maximilians Universität, München, 2001.
[7]. D. A. Keim and H. P. Kriegel: “Visualization Techniques for Mining Large Databases, A Comparison”, Transactions on Knowledge and Data Engineering, Special Issue on Data Mining, 1996.
[8]. D. A. Keim: “Technique Report of Data Mining”. Information for Site Planning, 2002.
[9]. D. A. Keim: “Designing pixel-oriented visualization techniques: Theory and applications”. Transaction Visualization Computer Graphic, 2000.
[10]. D. A. Keim, H. P. Kriegel, and M. Ankerst: “Recursive Pattern: A technique for visualizing very large amounts of data”. In Proc. of Visualization 95, Atlanta, GA, pages 279–286, 1995.
[11]. M. Ankerst, D. A. Keim, and H. P. Kriegel: “Circle Segments: A Technique for Visually Exploring Large Multidimensional Data Sets”. In Proc. of Visualization 96, Hot Topic Session, San Francisco, 1996.
[12]. D. Hand, H. Mannilla, and P. Smyth: “Principles of Data Mining”, MIT Press, Cambridge, Massachussets, 2001.
[13]. M. O. Ward: “XmdvTool : Integrating multiple methods for visualizing multivariate data”. In Proc. of Visualization 94, Washington, DC, pages 326–336, 1994.
[14]. H. Chernoff: “The Use of Faces to Represent Points in k-Dimensional Space Graphically”. Journal of the American Statistical Association, Vol. 68, pages 361-368, 1973.
[15]. M. P. Consens and A. O. Mendelzon: “Hy+: A Hygraph-based Query and Visualization System”, In Proc. of the ACM SIGMOD on Management of Data, 1993.
[16]. R. A. Becker, S. G. Eick, and G. J. Wills: “Visualizing Network Data”, IEEE Transactions on Visualizations and Graphics, 1995.
[17]. R. J. Hendley, N. S. Drew, A. M. Wood, R. Beale: “Narcissus: Visualizing Information”. In Proc. of Int. Sysmp. On IV, Atlanta, GA, pages 90-94, 1995.
[18]. S. Feiner and C. Beshers: “Visualizing n-Dimensional Virtual Worlds with n-Vision”, IEEE Computer Graphics, 1990.
[19]. J. LeBlanc, M. O. Ward, and N. Wittels: “Exploring N-Dimensional Databases”, In Proc. of Visualization ‘90, 1990.
[20]. B. Shneiderman: “Tree Visualization with Treemaps: A 2D Space-Filling Approach”, ACM Transactions on Graphics, 1992.
[21]. A. Inselberg and B. Dimsdale: “Parallel coordinates: a tool for visualizing multi-dimensional geometry”. In Proc. of the First IEEE Conference on Visualization, 1990.
[22]. K. Eser: “Visualizing Multi-dimensional Clusters, Trends, and Outliers using Star Coordinates”. In Proc. of the seventh ACM SIGKDD International Conference on Knowledge Discovery in Data, pages 107–116, 2001.
[23]. T. Christian; James A. and Heidrun S.: “Axes-Based Visualization with Radial Layouts”. In Proc. of the 2004 ACM Symposium on Applied Computing, 2004.
[24]. J. H. Holland: “Adaptation in Natural and Artificial Systems”, Ann Arbor: Univ. of Michigan Press, 1975.
[25]. D. Michie, D.J. Spiegelhalter and C.C. Taylor: “Machine Learning, Neural and Statistical Classification”. Ellis Horwood, 1994.
[26]. R. A. Fisher: “The use of multiple measurements in taxonomic problems”, Annual Eugenics, vol. 7, Part II, pages 179-188, 1936.
[27]. The data has been taken from the UCI Repository of Machine Learning Databases at ftp://ftp.ics.uci.edu/pub/machine-learning-databases.
[28]. T. Soon and M. Kwan: “StarClass: Interactive Visual Classification Using Star Coordinates” In Proc. of the 3rd SLAM International Conference on Data Mining, 2003.
指導教授 張嘉惠(Chia-Hui Chang) 審核日期 2005-7-13
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