Pattern discovery: A progressive visual analytic design to support categorical data analysis. (December 2017)

• Record Type: Journal Article
• Authors: Zhao, Hanqing; Zhang, Huijun; Liu, Yan; Zhang, Yongzhen; Zhang, Xiaolong (Luke)
• Is Part Of: Journal of visual languages & computing. Volume 43(2017)
• Page Start: 42
• Page End: 49
• Publication Date: 2017-12
• Subjects: Progressive -- Visual analytics -- Categorical data analysis; Visual programming languages (Computer science) -- Periodicals; Visual programming (Computer science) -- Periodicals; Programming languages (Electronic computers) -- Semantics -- Periodicals; Langages de programmation visuelle -- Périodiques; Programmation visuelle -- Périodiques; Langages de programmation -- Sémantique -- Périodiques; Programming languages (Electronic computers) -- Semantics; Visual programming (Computer science); Visual programming languages (Computer science); Periodicals; Electronic journals; 005
• DOI: 10.1016/j.jvlc.2017.05.004
• Languages: English
• ISSNs: 1045-926X

Abstract

Abstract: 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 progress. This requires the integration of data-mining algorithms with interactive tools to manipulate data and analytical process. This is where visual analytics can help. More than simple visualization of a dataset or some computation results, visual analytics 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.