"This book focuses on data mining for co-location pattern, a valid method for identifying patterns from all types of data and applying them in business intelligence and analytics. It explains the fundamentals of co-location pattern mining, co-location decision tree, and maximal instance co-location pattern mining along with an in-depth overview of data mining, machine learning, and statistics. This arrangement of chapters helps readers understand the methods of co-location pattern mining step-by-step andtheir applications in pavement management, image classification, geospatial buffer analysis, etc"--
Co-location pattern mining detects sets of features frequently located in close proximity to each other. This book focuses on data mining for co-location pattern, a valid method for identifying patterns from all types of data and applying them in business intelligence and analytics. It explains the fundamentals of co-location pattern mining, co-location decision tree, and maximal instance co-location pattern mining along with an in-depth overview of data mining, machine learning, and statistics. This arrangement of chapters helps readers understand the methods of co-location pattern mining step-by-step and their applications in pavement management, image classification, geospatial buffer analysis, etc.
This book focuses on data mining for co-location pattern, a valid method for identifying patterns from all types of data and applying them in business intelligence and analytics. It explains the fundamentals of co-location pattern mining, co-location decision tree, and maximal instance co-location pattern mining for a step-by-step understanding.
Chapter 1 Introduction
Chapter 2
Fundamentals of Mining Co-Location Patterns
Chapter 3
Principle of Mining Co-Location Patterns
Chapter 4
Manifold Learning Co-Location Pattern Mining
Chapter 5
Maximal Instance Co-Location Pattern Mining Algorithms
Chapter 6
Negative Co-Location Pattern Mining Algorithms
Chapter 7
Application of Mining Co-Location Patterns in Pavement Management and Rehabilitation
Chapter 8
Application of Mining Co-Location Patterns in Buffer Analysis
Chapter 9
Application of Mining Co-Location Patterns in Remotely Sensed Imagery Classification
Index
Guoqing Zhou received his first PhD from Wuhan University, Wuhan, China, in 1994 and his second PhD from Virginia Tech at Blacksburg, Virginia, USA, in 2001. He was a visiting scholar at the Department of Computer Science and Technology, Tsinghua University, Beijing, China, and a postdoctoral researcher at the Institute of Information Science, Beijing Jiaotong University, Beijing, China, from 19941996. He continued his research as an Alexander von Humboldt Fellow at the Technical University of Berlin, Berlin, Germany, from 19971998 and afterward became a postdoctoral researcher at the Ohio State University, Columbus, OH, USA, from 1998 to 2000. Later he worked at Old Dominion University, Norfolk, VA, USA, as an assistant professor, associate professor, and full professor in 2000, 2005, and 2010, respectively. He is currently professor at the Guilin University of Technology, Guilin, China. He is author of five books and has published more than 300 papers in peer-reviewed journals and conference proceedings.
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