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全二维气相飞行质谱数据
全二维气相飞行质谱数据 Text-based Visual analytics Convert LECO to CDF file Convert CDF files to 3D representation Manually preprocess data to eliminate uninteresting or confounding regions Visually explore the samples and perform noise removal operations Perform statistical analysis Perform statistical analysis Manually identify interesting potential biomarkers Visually identify interesting potential bio-markers Validate by checking the mass for each compound Visually validate by exploring the mass spectrum Run commercial samples of these potential biomarkers Visually explore the potential biomarkers of other samples
Page 1 and 2: 复杂有序数据的可视
Page 3 and 4: 复杂有序数据 • 顺序
Page 5 and 6: 可视化分析的挑战 •
Page 7 and 8: 时序轴的表达类型 •
Page 9 and 10: 不同的视图类型
Page 11 and 12: 2. 顺序数据之间的关
Page 13 and 14: PlanningLines 表达项目计
Page 15 and 16: TimeWheel
Page 17 and 18: 4. 动画与交互动画演
Page 19 and 20: 可视化分析案例 • 基
Page 21 and 22: 空气质量数据 • 数据
Page 23 and 24: 对四个季节的空气质
Page 25 and 26: 三维统计模板 : mean, med
Page 27 and 28: 集成可视化 : 检测异
Page 30 and 31: 全二维气相飞行质谱
Page 40 and 41: Motion tracks • 目标 - 区分
Page 42 and 43: 扩散张量成像数据 •
Page 44 and 45: DTI 纤维丛的交互可视
Page 48 and 49: 变形数据的可视分析
Page 50 and 51: 基于虚拟单词频率的
Page 52 and 53: 特征点 ( 虚拟单词 ) •
Page 54 and 55: 基于传输函数的特征
Page 57 and 58: 不同的特征聚类数目
Page 59 and 60: 飓风实例飓风伊莎贝
Page 61 and 62: Average density 平行 Timelines N
Page 64 and 65: 长文档的顺序可视化
Page 66 and 67: 长文档的顺序可视化
Page 68 and 69: 基于 “ 基因词袋 ” 的
Page 70 and 71: 基于事件分析的顺序
Page 72 and 73: 时变数据的事件图表
Page 74 and 75: 时变体数据的事件图
Page 78: 运动捕获数据的可视
Page 81: 致谢 • 合作者 - Song Zhan

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