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1 1 1.1............................ 1 1.2............................... 1 1.3.............................. 1 1.4......................... 2 1.4.1.................................. 2 1.4.2.......................... 2 1.4.3................................... 3 1.4.4............................... 4 1.5....................... 4 1.6................................... 4 1.7................................... 4 2 5 2.1........................... 5 2.1.1......................... 5 2.1.2................ 5 2.2........................ 6 2.2.1......................... 6 2.2.2................ 6 2.3....... 6 2.4......................... 7 2.5........................ 7 3 8 3.1...................... 8 3.1.1............................. 8 3.1.2............................. 8 3.2............................ 9 3.2.1............................... 9 4 10 i

4.1........................... 10 4.2.............................. 10 4.2.1............................... 10 4.3.............................. 10 4.3.1............................. 11 4.3.2........................... 11 4.4................................. 12 4.4.1................... 12 4.4.2.............................. 12 5 14 5.1................................... 14 5.1.1......................... 14 5.2............................. 15 5.2.1............................... 16 5.2.2................................ 16 5.2.3................................ 16 5.2.4............................... 18 5.2.5................................ 18 5.3........................................ 18 5.3.1 API................... 18 5.4................................... 19 5.4.1............................. 19 5.4.2................................ 20 5.4.3.......................... 20 5.4.4............................. 20 5.4.5............................... 21 5.5................................. 22 5.5.1............................. 22 5.5.2............................. 23 6 25 6.1................... 25 6.2....................... 29 7 32 7.1........................................ 32 7.2........................................ 32 7.2.1................................... 32 ii

7.2.2.................................. 32 7.3................................... 33 7.3.1.......................... 33 7.3.2........................ 33 7.3.3........................ 34 7.3.4................................ 34 7.4........................................ 35 7.5........................................ 35 7.6.................................... 37 7.6.1........................... 37 7.6.2.......................... 37 8 38 39 40 iii

1.1...................... 2 1.2................................... 3 4.1.................................. 11 4.2................................... 11 4.3............................. 11 4.4................................. 11 4.5............................... 11 4.6............................... 12 5.1........................ 14 5.2 ( )............................. 15 5.3................................... 16 5.4.................................... 17 5.5.................................... 17 5.6................................... 18 5.7................................. 19 5.8.............................. 19 5.9.................................... 20 5.10....................... 21 5.11................................. 21 5.12................................. 22 5.13......................... 23 5.14................................. 23 6.1 sex........................... 26 6.2 survived......................... 27 6.3 70s......................... 28 6.4........................... 30 6.5........................... 31 7.1........................... 36 iv

1 1.1 (Nominal variables) (Ordinal variables) (Quantitative variables) 3 [1] 2 1 2 1.2 (Categorical data) (Qualitative data) (Quantitative data) (Numerical data) (Attribute) (Category) 2 1.3 1

1.4 SPSS Microsoft Excel 1.1 1.1: 1.4.1 1.2 ( ) ( ) 1 2 1 1 1.4.2 2

1.2: 1.2 1.1 2 1.1 1.2 (Contingency Table) 1.1: 1 344 156 1.2: 1 10 85 92 20 83 89 30 88 87 40 144 93 50 144 93 1.4.3 1 3 3

1.4.4 3 (Dimension) (Multidimensional data) (Multivariate data) 3 10 1.5 1 3 3 1.6 1.7 3 4

2 4 2.1 2.1.1 Parallel coordinates[2] 2.1.2 SCATTERDICE[3] 2 1 3 2 Dust & Magnet[4] SGVIEWR[5] Parallel coordinates[2] Parallel Sets[6] 5

2.2 2.2.1 Cobweb diagram[7] Mosaic Display[8] 2 3 Cattrees[9] Treemap[10] Treemap Cattrees Treemap Hammock Plots[11] Parallel coordinates 2.2.2 Parallel sets[6] Parallel coordinates[2] Mosaic Display[8] Parallel coordinates 3 Parallel sets SQiRL[12] SQiRL & 2.3 Trellis Display[13] Pixel Bar Chart[14] Hierarchical Pixcel Bar Chart[15] Pixel Bar Chart Table Lens[16] 6

2.4 SellTrend[17] Treemap Set o gram[18] FanLens[19] 2.5 Polaris[20] & XmdvTool[21] GGobi[22] Parallel coodinates Brushing Zoom 7

3 3.1 1 2 3.1.1 2 2 1 2 3.1.2 3 3 3 8

10 AND 3.2 Shneiderman Mantra[23] 3.2.1 1 10 20 Microsoft Excel & 9

4 4.1 2 10 20 AND 4.2 4.2.1 4.1 4.2 4.1 4.3 10

4.1: 4.2: [24] 4.3 4.3: 4.4: 4.5: 4.3.1 [24] 4.4 2 4.3.2 [24] 4.5 3 11

4.4 3.2.1 4.4.1 ( 4.6 ) 10 10 10 10 10 ( 4.6 ) 20 20 20 ( 4.6 ) 4.6: 4.4.2 [25] 12

13

5 5.1 5.1.1 Linking&Brushing[26] Linking[27] Linking Brushing[28] Brushing ( 5.1) 5.1: 14

5.2 5.2 5.2: ( ) 15

5.2.1 ( 5.3) 5.3: 5.2.2 ( 5.4) 5.2.3 ( 5.5) 16

5.4: 5.5: 17

5.2.4 ( 5.6) Bar Stacked Stacked100 100% 5.6: 5.2.5 5.3 5.3.1 API C#(Microsoft.NET Framework 3.5) API Microsoft Chart Controls CSV 18

5.4 5.4.1 4.4 & ( 5.7) 5.7: 30 40 2 10 20 30 30 40 50 60 40 ( 5.8) 5.8: 19

5.4.2 ( 5.9 ) 5.9: 5.4.3 5.10 5.4.4 10 10 10 ( 5.11) 10 20 30 10 2 20

5.10: 5.11: 5.4.5 ( 5.6) 21

5.5 (Forcebased algorithms)[29][30] 2 5.5.1 5.12 3 ( 5.13) 5.12: Algorithm1 N,E 1, E 2,, E k distance(c i, C j ) C i C j radius(e k ) E k Algorithm 1 loop for all E i N do v = (0, 0) for all E j N and E i E j do distance distance(e i, E j ) if distance < radius(e i ) + radius(e j ) then Caluculate a vector v ij from E i to E j v v + v ij end if end for Move E i in the direction of v end for end loop 22

5.13: 5.5.2 5.14 3 5.14: Algorithm2 Label k 23

Algorithm 2 repeat for all E i N do distance distance(e i, Label) Caluculate a vector v from E i to Label Move E i in the direction of v distance k end for until Mousebutton is released 24

6 6.1 1 6.1 6.1: class 1st, 2nd, 3rd sex female, male survived survived, died age age categorized 10 10s, 20s, 30s,, 90s embarked home.dest / room ticket boat female male 2 femalemale ( 6.1) 6.1 survived surviveddied survived died 1 http://www.statsci.org/datasets.html 25

6.1: sex 6.2 class 3rd 20% 1st 60% sex 3 age categorized child 70s 100% 70s 70s 70s 70s ( 6.3) 70s 1st 2nd3rd1st 26

6.2: survived 27

6.3: 70s 28

6.2 2 10 A B C 6.2 6.2: Q1 Q2 Q3 PHS Q4 Q5 Q6 Q7 Q8PHS Q9PHS Q10PHS Q6 Q11 Q12. Q13 2 2 (http://research.netmile.co.jp/) SPSS Japan(http://www.spss.co.jp) SPSS 29

( 6.4) 6.4: Q1 A C B A C B Q1 A B C 3 ( 6.5) C 40 A 10 30 C B A C 30

6.5: 31

7 7.1 2. 7.2 5 Microsoft Excel 2007 7.2.1 6 7.2.2 1. 2. 10 3. 5 4. 32

7.3 3 7.3.1 ( ) Q1 A 30 40 7.3.2 ( ) Q4 Q8 Q12 150 300 300 500 33

7.3.3 ( ) Q3 B 1 2 ( 1) Q2 2 3 ( 2) Q2 4 5 7.3.4 7.1 8 7.1: 1 2 3 4 5 6 7 8 2 2 1 2 4 3 2 2 2 3 4 2 2 34

( ) ( ) 7.4 7.2 Pivot table + Our tool + 7.2: 1 2 3 4 5 6 Pivot table 1 4 1 null 3 4 5 3.4 Pivot table 2 2 2 2 2 4 3 2.5 Pivot table 3 2 1 4 3 4 5 3.4 Pivot table 4 2 1 4 2 5 4 3 Pivot table 5 1 1 4 2 4 3 2.5 Pivot table 6 2 1 2 2 3 3 2.2 Pivot table 7 2 1 4 2 2 2 2.2 Pivot table 8 2 2 4 2 5 3 3 Our tool 1 4 5 4 5 5 4 4.5 Our tool 2 3 4 4 4 5 4 4 Our tool 3 3 3 4 4 5 4 3.8 Our tool 4 2 2 3 5 4 5 3.5 Our tool 5 3 2 3 4 5 5 3.7 Our tool 6 2 4 4 4 5 5 4 Our tool 7 2 2 5 4 5 4 3.7 Our tool 8 1 1 3 4 5 2 2.7 5= 4= 3= 2= 1= 7.5 ( 7.1) 1 7 35

7.1: 7.3: 1 2 3 4 5 6 5 3 4 4 5 5 4.4 2 3 3 3 1 1 2.2 3 5 4 2 2 3 3.2 1 2 3 4 5 6 1 1 2 1 1 2 1.4 4 5 4 5 5 5 4.7 4 5 4 4 5 5 4.5 5= 4= 3= 2= 1= 8 8 36

2 4 7.6 7.6.1 7.6.2 n O(n 2 ) 37

8 38

2 NAIS WAVE Ubiquitous 2 39

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[12] Geoffrey Draper and Richard Riesenfeld. Who votes for what? a visual query language for opinion data. IEEE Transactions on Visualization and Computer Graphics, Vol. 14, No. 6, pp. 1197 1204, 2008. [13] Ra Becker, Cleveland WS, and Shyu M-J. The design and control of trellis display. Journal of Computational and Statistical Graphics, No. 5, pp. 123 155, 1996. [14] Daniel Keim, Ming Hao, Umesh Dayal, Meichun Hsu, and Julain Ladisch. Pixel bar charts: A new technique for visualizing large multi-attribute data sets without aggregation. IEEE Symposium on Information Visualization, p. 113, 2001. [15] Daniel A.Keim, Ming C.Hao, and UmeshwarDayal. Hierarchical pixel bar charts. IEEE Transactions on Visualization and Computer Graphics, Vol. 8, No. 03, pp. 255 269, 2002. [16] Ramana Rao and Stuart K. Card. The table lens: merging graphical and symbolic representations in an interactive focus + context visualization for tabular information. In CHI 94: Proceedings of the SIGCHI conference on Human factors in computing systems, pp. 318 322, New York, NY, USA, 1994. ACM. [17] Zhicheng Liu, John Stasko, and Timothy Sullivan. Selltrend: Inter-attribute visual analysis of temporal transaction data. IEEE Transactions on Visualization and Computer Graphics, Vol. 15, No. 6, pp. 1025 1032, 2009. [18] Wolfgang Freiler, Kresimir Matkovic, and Helwig Hauser. Interactive visual analysis of settyped data. IEEE Transactions on Visualization and Computer Graphics, Vol. 14, No. 6, pp. 1340 1347, 2008. [19] Shixia Liu Xinghua Lou and Tianshu Wang. Fanlens: A visual toolkit for dynamically exploring the distribution of hierarchical attributes. In Visualization Symposium, 2008. PacificVIS 08. IEEE Pacific, pp. 151 158, 2008. [20] Chris Stolte and Pat Hanrahan. Polaris: A system for query, analysis and visualization of multi-dimensional relational databases. IEEE Transactions on Visualization and Computer Graphics, Vol. 8, pp. 52 65, 2002. [21] Matthew O. Ward. Xmdvtool: integrating multiple methods for visualizing multivariate data. In VIS 94: Proceedings of the conference on Visualization 94, pp. 326 333, Los Alamitos, CA, USA, 1994. IEEE Computer Society Press. [22] Deborah F. Swayne, Duncan Temple Lang, Andreas Buja, and Dianne Cook. Ggobi: evolving from xgobi into an extensible framework for interactive data visualization. Comput. Stat. Data Anal., Vol. 43, No. 4, pp. 423 444, 2003. 41

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