Pattern discovery: A progressive visual analytic design to support categorical data analysis

Hanqing Zhao, Huijun Zhang, Yan Liu, Yongzhen Zhang, Xiaolong (Luke) Zhang

Research output: Contribution to journalArticle

3 Citations (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)42-49
Number of pages8
JournalJournal of Visual Languages and Computing
Volume43
DOIs
StatePublished - Dec 2017

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Data mining
Visualization
Statistical methods
Categorical
Feedback
Data Mining

All Science Journal Classification (ASJC) codes

  • Language and Linguistics
  • Human-Computer Interaction
  • Computer Science Applications

Cite this

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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.",
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Pattern discovery : A progressive visual analytic design to support categorical data analysis. / Zhao, Hanqing; Zhang, Huijun; Liu, Yan; Zhang, Yongzhen; Zhang, Xiaolong (Luke).

In: Journal of Visual Languages and Computing, Vol. 43, 12.2017, p. 42-49.

Research output: Contribution to journalArticle

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