Analysis of Graphic Language Expression in Visual Communication Design.

Area of Science:

• Data Science
• Information Visualization
• Human-Computer Interaction

Background:

• Modern society requires more than verbal communication, especially in the data era.
• Transforming data into visual language is crucial for effective information exchange.
• Existing methods may not adequately address the intuitive understanding of complex, high-dimensional data.

Purpose of the Study:

• To design and develop a data-based intelligent system for multi-dimensional data visualization.
• To enable intuitive comprehension of high-dimensional data through visual representation.
• To optimize the user experience in data exploration and analysis.

Main Methods:

• Dimensionality reduction techniques applied to high-dimensional datasets.
• Transformation of low-dimensional data into intuitive graphic markers.
• Development of an intelligent system to suggest appropriate visual labels.

Main Results:

• The system successfully reduces data dimensions for easier interpretation.
• Low-dimensional data is effectively converted into user-friendly graphic markers.
• Users receive appropriate label suggestions, saving time and cognitive effort.

Conclusions:

• The developed intelligent system significantly improves the intuitive understanding of high-dimensional data.
• Effective data visualization enhances user efficiency and experience.
• Addressing user-specific needs in data visualization remains a key challenge for optimal effectiveness.