IEEE Transactions on Big Data
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From the April-June 2018 issue
An Enhanced Visualization Method to Aid Behavioral Trajectory Pattern Recognition Infrastructure for Big Longitudinal Data
By Hua Fang and Zhaoyang Zhang
Big longitudinal data provide more reliable information for decision making and are common in all kinds of fields. Trajectory pattern recognition is in an urgent need to discover important structures for such data. Developing better and more computationally-efficient visualization tool is crucial to guide this technique. This paper proposes an enhanced projection pursuit (EPP) method to better project and visualize the structures (e.g., clusters) of big high-dimensional (HD) longitudinal data on a lower-dimensional plane. Unlike classic PP methods potentially useful for longitudinal data, EPP is built upon nonlinear mapping algorithms to compute its stress (error) function by balancing the paired weights for between and within structure stress while preserving original structure membership in the high-dimensional space. Specifically, EPP solves an NP hard optimization problem by integrating gradual optimization and non-linear mapping algorithms, and automates the searching of an optimal number of iterations to display a stable structure for varying sample sizes and dimensions. Using publicized UCI and real longitudinal clinical trial datasets as well as simulation, EPP demonstrates its better performance in visualizing big HD longitudinal data.
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- State of the Journal Editorial (Jan-March 2018)
- State of the Journal Editorial (Jan-March 2017)
- Welcome to the IEEE Transactions on Big Data (Jan-March 2015)
- Introduction to the IEEE Transactions on Big Data (Jan-March 2015)
- Guest Editorial: Big Data Infrastructure I (April-June 2018)
- Special Issue on Biomedical Big Data: Understanding, Learning and Applications (Oct-Dec 2017)
- Urban Computing (April-June 2017)
- Big Scholar Data Discovery and Collaboration (Jan-March 2017)
- Big Data Analytics and the Web (July-Sept 2016)
- Big Scholar Data Discovery and Collaboration (Continued) (April-June 2016)
- Big Scholar Data Discovery and Collaboration (Jan-March 2016)
- Big Data Analytics and the Web (Oct-Dec 2015)
- Big Media Data: Understanding, Search, and Mining (Part 2) (Oct-Dec 2015)
- Big Media Data: Understanding, Search, and Mining (July-Sept 2015)
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