Visual Data Mining: Interactive Visualization Techniques for Knowledge Discovery and Decision Support

Abstract

Visual Data Mining is an interdisciplinary approach that integrates data mining algorithms with advanced visualization techniques to support effective knowledge discovery from large and complex datasets. As the volume, variety, and velocity of data continue to grow, traditional analytical methods often struggle to reveal hidden patterns and meaningful insights. Visual data mining addresses this challenge by combining the computational power of machine learning and data mining with human visual perception and interactive exploration. This approach enables users to identify trends, correlations, anomalies, and structures that may not be easily detected through automated analysis alone. The process typically involves data preprocessing, dimensionality reduction, pattern extraction, and interactive visualization, allowing analysts to iteratively refine their understanding of the data. Techniques such as scatter plots, heat maps, parallel coordinates, network graphs, and dashboard-based visual analytics play a crucial role in enhancing interpretability and user engagement. Visual data mining is widely applied across various domains, including business intelligence, healthcare analytics, bioinformatics, finance, cybersecurity, and social network analysis. By supporting intuitive exploration and hypothesis generation, it bridges the gap between raw data and actionable knowledge. Furthermore, the integration of interactive visual interfaces with data mining models improves transparency, trust, and decision-making efficiency. As data-driven systems increasingly demand explainability and user involvement, visual data mining has become a vital tool for transforming complex datasets into comprehensible and valuable insights.