The chapter "Data and Dimensionality Reduction in Data Analysis and System Modeling" by WITOLD PEDRYCZ discusses the importance of data and dimensionality reduction in the context of data analysis and system modeling. With the rapid growth of data sizes and the increasing diversity of data, reduction mechanisms are essential to manage and understand the data effectively. Data reduction focuses on reducing the number of data points to reveal underlying structures, often through clustering techniques. Dimensionality reduction aims to reduce the number of attributes or features, often transforming the data into a lower-dimensional space. The chapter outlines the historical development of these techniques, from classic statistical methods like principal component analysis to more advanced optimization techniques such as tabu search and biologically-inspired methods. It also introduces the concept of information granularity and granular computing, which are fundamental to both data and feature reduction. The chapter covers various reduction processes, including clustering, feature transformation, and optimization, and discusses the criteria for evaluating the quality of reduced feature spaces, such as filters and wrappers. Finally, it provides a roadmap for dimensionality reduction, emphasizing the importance of guided reduction activities within formal frameworks of information granules.The chapter "Data and Dimensionality Reduction in Data Analysis and System Modeling" by WITOLD PEDRYCZ discusses the importance of data and dimensionality reduction in the context of data analysis and system modeling. With the rapid growth of data sizes and the increasing diversity of data, reduction mechanisms are essential to manage and understand the data effectively. Data reduction focuses on reducing the number of data points to reveal underlying structures, often through clustering techniques. Dimensionality reduction aims to reduce the number of attributes or features, often transforming the data into a lower-dimensional space. The chapter outlines the historical development of these techniques, from classic statistical methods like principal component analysis to more advanced optimization techniques such as tabu search and biologically-inspired methods. It also introduces the concept of information granularity and granular computing, which are fundamental to both data and feature reduction. The chapter covers various reduction processes, including clustering, feature transformation, and optimization, and discusses the criteria for evaluating the quality of reduced feature spaces, such as filters and wrappers. Finally, it provides a roadmap for dimensionality reduction, emphasizing the importance of guided reduction activities within formal frameworks of information granules.