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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.