Visual Data Analysis using Tracked Statistical Measures within Parallel Coordinate Representations
Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
With our increasing ability to capture and store large multivariate data, these data sets are increasing in size and complexity. Traditionally, data sets from various areas of the society are examined using sophisticated mathematical techniques in order to discover strategic information hidden in the large amount of data. In addition to these automatic methods, a number of advanced techniques have been developed for the purpose of visualizing multivariate data, and to give the user a visual understanding of the data. Many of these techniques encounter problems like cluttered displays, as they are not designed to handle the amounts of entries that are stored in today's databases and data warehouses. This report investigates the current research situation of methods that address the problem of overplotted displays. A novel method called Visual Data Mining Display (VDMD) is presented, to overcome the stated problem by interactively selecting and displaying statistics of the data in a separate view. Changes in the display are visually tracked by animation and vector plotting for easy comparison of statistical values and subsets of the data. The method has proved helpful in providing an overview of large data sets, as well as in observing changes of the distribution in each dimension of the data.
Place, publisher, year, edition, pages
2005. , 64 p.
Engineering and Technology
IdentifiersURN: urn:nbn:se:liu:diva-97779ISRN: LITH-ITN-MT-EX--05/030--SEOAI: oai:DiVA.org:liu-97779DiVA: diva2:652622
Subject / course