Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Visual exploration of multivariate data typically requires projection onto lor dimensional representations. The number of possible representations grows rapidly with the number of dimensions, and manual exploration quickly becomes ineffective or even unfeasible.  proposes automatic analysis methods to extract potentially relevant visual structures from a set of candidate visualizations. Based on features, the visualizations are ranked in accordance with a specified user task. The user is provided with a manageable number of potentially useful candidate visualizations, which can be used as a starting point for interactive data analysis. This can effectively ease the task of finding truly useful visualizations and potentially speed up the data exploration task. ,  present ranking measures for class-based as ll as non-class-based scatterplots and parallel coordinates visualizations. The proposed analysis methods are evaluated on different data sets.
['Andrada Tatu', 'Georgia Albuquerque', 'Martin Eisemann', 'Peter Bak', 'Holger Theisel', 'Marcus A. Magnor', 'Daniel A. Keim']
I am writing a research paper on a idea Automated Analytical Methods to Support Visual Exploration of High-Dimensional Data
[{"from": "human", "value": "I am writing a research paper on a idea Automated Analytical Methods to Support Visual Exploration of High-Dimensional Data"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Visual exploration of multivariate data typically requires projection onto lor dimensional representations. The number of possible representations grows rapidly with the number of dimensions, and manual exploration quickly becomes ineffective or even unfeasible.  proposes automatic analysis methods to extract potentially relevant visual structures from a set of candidate visualizations. Based on features, the visualizations are ranked in accordance with a specified user task. The user is provided with a manageable number of potentially useful candidate visualizations, which can be used as a starting point for interactive data analysis. This can effectively ease the task of finding truly useful visualizations and potentially speed up the data exploration task. ,  present ranking measures for class-based as ll as non-class-based scatterplots and parallel coordinates visualizations. The proposed analysis methods are evaluated on different data sets."}]