Dealing with knowledge discovery and data mining in spatial and temporal data, this book seeks to present novel methods that can be employed to discover spatial structures and processes in complex data. Numerical examples and applications are used throughout.

When I ?rst came across the term data mining and knowledge discovery in databases, I was excited and curious to ?nd out what it was all about. I was excited because the term tends to convey a new ?eld that is in the making. I was curious because I wondered what it was doing that the other ?elds of research, such as statistics and the broad ?eld of arti?cial intelligence, were not doing. After reading up on the literature, I have come to realize that it is not much different from conventional data analysis. The commonly used de?nition of knowledge discovery in databases: the non-trivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data is actually in line with the core mission of conventional data analysis. The process employed by conventional data analysis is by no means trivial, and the patterns in data to be unraveled have, of course, to be valid, novel, useful and understandable. Therefore, what is the commotion all about? Careful scrutiny of the main lines of research in data mining and knowledge discovery again told me that they are not much different from that of conventional data analysis. Putting aside data warehousing and database m- agement aspects, again a main area of research in conventional database research, the rest of the tasks in data mining are largely the main concerns of conventional data analysis.

Includes supplementary material: sn.pub/extras

Autorentext

Yee Leung is Emeritus Professor of the Department of Geography and Resource Management and Honorary Senior Research Fellow of the Institute of Space and Earth Information Science at the Chinese University of Hong Kong, Hong Kong SAR, P. R. China. He is Pioneer of the theory and applications of fuzzy sets approach to geographical research and has done novel research on uncertainty analysis in geographical modeling and spatial information involving probability, statistics, fuzzy set, rough set, and granular computing. His research also covers artificial intelligence in general and machine learning including deep learning, neural network, evolutionary computation, and optimization in particular. He has published six monographs and more than 200 articles in reputed international journals and book chapters mainly in geography, geographical information science, artificial intelligence, computer science, and information science. His research is holistically reflected in the landmark research monographs.

Inhalt
Discovery of Intrinsic Clustering in Spatial Data.- Statistical Approach to the Identification of Separation Surface for Spatial Data.- Algorithmic Approach to the Identification of Classification Rules or Separation Surface for Spatial Data.- Discovery of Spatial Relationships in Spatial Data.- Discovery of Structures and Processes in Temporal Data.- Summary and Outlooks.