Interactive Visual Summarization for Visualizing Large, Multidimensional Datasets

visualization;data management;data mining

Kocherlakota, Sarat Mohan ; Dr. Rada Y. Chirkova, Committee Member,Dr. Thomas L. Honeycutt, Committee Member,Dr. Christopher G. Healey, Committee Chair,Dr. Xiaosong Ma, Committee Member,Kocherlakota, Sarat Mohan ; Dr. Rada Y. Chirkova ; Committee Member ; Dr. Thomas L. Honeycutt ; Committee Member ; Dr. Christopher G. Healey ; Committee Chair ; Dr. Xiaosong Ma ; Committee Member

University:North Carolina State University

关键词: visualization;    data management;    data mining;   

Others  :  https://repository.lib.ncsu.edu/bitstream/handle/1840.16/3908/etd.pdf?sequence=1&isAllowed=y

美国|英语

来源: null

【 摘 要 】

Because of its ability to help users analyze and explore data from a diverse set of domains, visualization is becoming integral to the knowledge discovery process. However, existing visualization techniques for displaying large, multidimensional datasets often produce detailed, cluttered images that overwhelm the user's ability to effectively absorb the underlying data.To visualize such datasets effectively we have developed a visual summarization framework that intelligently summarizes datasets by extracting its important and relevant characteristics prior to visualization. The summaries are then visualized both in place of the original data, or along with the original data. Our approach performs this summarization in three broad steps. First, size and dimensionality of the data are reduced meaningfully. Next, patterns and dependencies in the form of association rules, along with outliers are extracted from the reduced data. Finally, these summary characteristics are visualized using techniques that are aimed at enhancing the comprehension of the data. Summary characteristics, as well as summarization steps are also recorded. Our framework is designed to harness the benefits of both visual and non-visual methods to intuitively guide users to produce relevant data summaries. Initial results in applying our approach to practical datasets suggest that our approach could be used to generate effective visual summaries of large, multidimensional datasets from a wide variety of domains and applications.