Data Mining for Co-location Patterns
Principles and Applications
1st Edition
Published on 26. January 2022
228 pages
978-1-000-53343-9 (ISBN)
Description
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.
More details
Edition
1. Auflage
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
College/higher education
Professional and scholarly
Illustrations
44 Tables, black and white; 88 Line drawings, black and white; 46 Halftones, black and white; 134 Illustrations, black and white
ISBN-13
978-1-000-53343-9 (9781000533439)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Book
05/2024
1st Edition
CRC Press
€71.30
Shipment within 15-20 days
Book
01/2022
1st Edition
CRC Press
€170.20
Shipment within 15-20 days
Person
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 1994-1996. He continued his research as an Alexander von Humboldt Fellow at the Technical University of Berlin, Berlin, Germany, from 1997-1998 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.
Content
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
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
