pattern discovery: a progressive visual analytic system design to support categorical data analysis

Open Access
Liu, Yan
Graduate Program:
Industrial Engineering
Master of Science
Document Type:
Master Thesis
Date of Defense:
Committee Members:
  • Ling Rothrock, Thesis Advisor
  • Xiaolong Zhang, Thesis Advisor
  • visual analytics; progressive visualization; pattern discovery;
When using data-mining tools to analyze big data, users often need tools to support the understanding of individual data attributes and control the analysis progresses. This requires the integration of data-mining algorithms with interactive tools to manipulate data and analytical processes. This is where visual analytics can help. Visual analytics is the science that combines strong computation power of machine and perceptual intuition of visualization techniques to facilitate data analysis in understanding, reasoning and decision making [1]. More than simple visualization of a dataset or some computation results, it provides users an environment to iteratively explore different inputs or parameters and see the corresponding results. In this research, we explore a design of progressive visual analytics to support the analysis of categorical data with a data-mining algorithm, Apriori. Our study focuses on executing data mining techniques step-by-step and showing intermediate result at every stage to facilitate sense-making. Our design, called Pattern Discovery Tool, targets for a medical dataset. Starting with visualization of data properties and immediate feedback of users’ inputs or adjustments, Pattern Discovery Tool could help users detect interesting patterns and factors effectively and efficiently. Afterward, further analyses such as statistical methods could be conducted to test those possible theories.