A Visual Analytics Approach for Correlation, Classification, and Regression Analysis

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Author(s): Chad A. Steed (Oak Ridge National Laboratory, USA), J. Edward Swan (Bagley College of Engineering, Mississippi State University, USA), Patrick J. Fitzpatrick (Northern Gulf Institute, Mississippi State University, USA)and T.J. Jankun-Kelly (Bagley College of Engineering, Mississippi State University, USA)
Copyright: 2014
Pages: 21
Source title: Innovative Approaches of Data Visualization and Visual Analytics
Source Author(s)/Editor(s): Mao Lin Huang (University of Technology, Sydney, Australia)and Weidong Huang (CSIRO, Australia)
DOI: 10.4018/978-1-4666-4309-3.ch002

Keywords: Data Analysis & Statistics / Data Mining and Databases / Information Science Reference / Library & Information Science

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Abstract

New approaches that combine the strengths of humans and machines are necessary to equip analysts with the proper tools for exploring today’s increasingly complex, multivariate data sets. In this chapter, a visual data mining framework, called the Multidimensional Data eXplorer (MDX), is described that addresses the challenges of today’s data by combining automated statistical analytics with a highly interactive parallel coordinates based canvas. In addition to several intuitive interaction capabilities, this framework offers a rich set of graphical statistical indicators, interactive regression analysis, visual correlation mining, automated axis arrangements and filtering, and data classification techniques. This chapter provides a detailed description of the system as well as a discussion of key design aspects and critical feedback from domain experts.

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A Visual Analytics Approach for Correlation, Classification, and Regression Analysis

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