Data mining for patterns and knowledge discovery in multivariate datasets are very important processes and tasks to help analysts understand the dataset, describe the dataset, and predict unknown data values. However, conventional computer-supported data mining approaches often limit the user from getting involved in the mining process and performing interactions during the pattern discovery. Besides, without the visual representation of the extracted knowledge, the analysts can have difficulty explaining and understanding the patterns. Therefore, instead of directly applying automatic data mining techniques, it is necessary to develop appropriate techniques and visualization systems that allow users to interactively perform knowledge discovery, visually examine the patterns, adjust the parameters, and discover more interesting patterns based on their requirements. In the dissertation, I will discuss different proposed visualization systems to assist analysts in mining patterns and discovering knowledge in multivariate datasets, including the design, implementation, and the evaluation. Three types of different patterns are proposed and discussed, including trends, clusters of subgroups, and local patterns. For trend discovery, the parameter space is visualized to allow the user to visually examine the space and find where good linear patterns exist. For cluster discovery, the user is able to interactively set the query range on a target attribute, and retrieve all the sub-regions that satisfy the user's requirements. The sub-regions that satisfy the same query and are neareach other are grouped and aggregated to form clusters. For local pattern discovery, the patterns for the local sub-region with a focal point and its neighbors are computationally extracted and visually represented. To discover interesting local neighbors, the extracted local patterns are integrated and visually shown to the analysts. Evaluations of the three visualization systems using formal user studies are also performed and discussed.

"Many approaches to the visualization of multivariate data have been proposed to date. Pixel oriented techniques map each attribute value of the data to a single colored pixel, theoretically yielding the display of the maximum possible information at a time. A large number of pixel layout methods have been proposed, each of which enables users to perform their visual exploration tasks to varying degrees. Pixel oriented techniques typically maintain the global view of large amounts of data while still preserving the perception of small regions of interest, which makes them particularly interesting for visualizing very large multidimensional data sets. Pixel based methods also provide feedback on the given query by presenting not only the data items fulfilling the query but also the data that approximately fulfill the query. The goal of this thesis was to extend XmdvTool, a public domain multivariate data visualization package, to incorporate pixel based techniques and to explore their strengths and weaknesses. The main challenge here was to seamlessly apply the interaction and distortion techniques used in other visualization methods within XmdvTool to pixel based methods and investigate the capabilities made possible by fusing the various multivariate visualization techniques."

Cyclones are complex weather phenomena, affected by multiple variables such as pressure, wind, temperature and more. Therefore, how cyclones are formed, what affects them and how they can be tracked is still actively researched today. Cyclones can have multiple centers (eyes), which can split and merge during its lifetime, which make them even more complex to define mathematically. In this thesis, how multi-center cyclones can be meaningfully visualized for domain scientists using multivariate visualization is investigated. An important aspect of the visualization is how a cyclone’s spread and boundary can be defined. The result is a visualization where the cyclonic region is defined by segmenting a pressure volume, and then a surface is extracted to get the cyclone’s boundary. Temperature is visualized using color mapping onto surfaces, while the wind velocity is shown using particles. The framework allows domain scientists to affect the visualization by picking criteria for segmenting the volume, color maps, and more. In conclusion, an improved cyclonic region could be defined by using multiple fields instead of only pressure, and the visualization would be improved with a greater detail put into the wind part. / <p>Examensarbetet är utfört vid Institutionen för teknik och naturvetenskap (ITN) vid Tekniska högskolan, Linköpings universitet</p>