文件名称：Visual Knowledge Discovery and Machine Learning
文件大小：12.94MB
文件格式：PDF
更新时间：2021-08-19 16:51:07
机器学习
Emergence of Data Science placed knowledge discovery, machine learning, and data mining in multidimensional data, into the forefront of a wide range of current research, and application activities in computer science, and many domains far beyond it. Discovering patterns, in multidimensional data, using a combination of visual and analytical machine learning means are an attractive visual analytics opportunity. It allows the injection of the unique human perceptual and cognitive abilities, directly into the process of discovering multidimensional patterns. While this opportunity exists, the long-standing problem is that we cannot see the n-D data with a naked eye. Our cognitive and perceptual abilities are perfected only in the 3-D physical world. We need enhanced visualization tools (“n-D glasses”) to represent the n-D data in 2-D completely, without loss of information, which is important for knowledge discovery. While multiple visualization methods for the n-D data have been developed and successfully used for many tasks, many of them are non-reversible and lossy. Such methods do not represent the n-D data fully and do not allow the restoration of the n-D data completely from their 2-D representation. Respectively, our abilities to discover the n-D data patterns, from such incomplete 2-D representations, are limited and potentially erroneous. The number of available approaches, to overcome these limitations, is quite limited itself. The Parallel Coordinates and the Radial/Star Coordinates, today, are the most powerful reversible and lossless n-D data visualization methods, while suffer from occlusion. There is a need to extend the class of reversible and lossless n-D data visual representations, for the knowledge discovery in the n-D data. A new class of such representations, called the General Line Coordinate (GLC) and several of their specifications, are the focus of this book. This book describes the GLCs, and their advantages, which include analyzing the data of the Challenger disaster, World hunger, semantic shift in humorous texts, image processing, medical computer-aided diagnostics, stock market, and the currency exchange rate predictions. Reversible methods for visualizing the n-D data have the advantages as cognitive enhancers, of the human cognitive abilities, to discover the n-D data patterns.