Visual Analytics - Introduction (2)

This video lecture elaborates on the role of visual analytics in knowledge discovery. Visual analytics solutions lead to new hypothesis and to findings but a lot of further analysis, even involving new data may be necessary to arrive at trusted knowledge. The filtering step of visual analytics is explained, including outlier detection and aggregation of different data. Data cleansing is discussed as a family of techniques to deal with incomplete or unreliable data. With respect to analytics, the role of clustering is discussed. Spatio-temporal visual analytics is explained by means of the Corona-Map. An essential trade-off in designing visual analytics systems relates to flexibility and guidance, where flexibility emphasizes the power of a system, whereas guidance aims at reducing the complexity to a manageable level. Different levels of guidance are provided. Chapters: 00:00 - Components of Visual Analytics 06:11 - Strategies and Principles 26:08 - Reproducibility 30:30 - Pair and Cooperative Visual Analytics 35:11 - Progressive Visual Analytics 38:35 - Major Application Areas 51:20 - Further Information and References