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Preference-based Spatial Co-location Pattern Mining 1st ed. 2022 Edition
Author(s): Lizhen Wang (Author), Yuan Fang (Author), Lihua Zhou (Author)
- Publisher: Springer
- Publication Date: 6 Jan. 2023
- Edition: 1st ed. 2022
- Language: English
- Print length: 310 pages
- ISBN-10: 9811675686
- ISBN-13: 9789811675683
Book Description
Preference-based co-location pattern mining refers to mining constrained or condensed co-location patterns instead of mining all prevalent co-location patterns. Based on the authors’ recent research, the book highlights techniques for solving a range of problems in this context, including maximal co-location pattern mining, closed co-location pattern mining, top-k co-location pattern mining, non-redundant co-location pattern mining, dominant co-location pattern mining, high utility co-location pattern mining, user-preferred co-location pattern mining, and similarity measures between spatial co-location patterns.
Presenting a systematic, mathematical study of preference-based spatial co-location pattern mining, this book can be used both as a textbook for those new to the topic and as a reference resource for experienced professionals.
Editorial Reviews
From the Back Cover
Preference-based co-location pattern mining refers to mining constrained or condensed co-location patterns instead of mining all prevalent co-location patterns. Based on the authors’ recent research, the book highlights techniques for solving a range of problems in this context, including maximal co-location pattern mining, closed co-location pattern mining, top-k co-location pattern mining, non-redundant co-location pattern mining, dominant co-location pattern mining, high utility co-location pattern mining, user-preferred co-location pattern mining, and similarity measures between spatial co-location patterns.
Presenting a systematic, mathematical study of preference-based spatial co-location pattern mining, this book can be used both as a textbook for those new to the topic and as a reference resource for experienced professionals.
About the Author
Fang, Yuan received her BS and MSc degrees in computer science from Nanjing Agricultural University, in 2008 and 2014, respectively, and her PhD degree in computer science from the Yunnan University, in 2018. She is currently a postdoctoral follow of South-Western Institute for Astronomy Research (SWIFAR), Yunnan University. She has published 15 papers on data mining in various journals and at conferences. Her research interests include spatial data mining, big data analytics and their applications.
Zhou, Lihua received her BS and MSc degrees in information and electronic science from Yunnan University in 1989 and 1992 respectively, and her PhD degree in communication and information system from Yunnan University in 2010. She is currently a professor at the School of Computer Science and Engineering, Yunnan University. She has published more than 50 papers on data mining in various journals and at conferences.
