Overview
- Clear and precise definitions of basic concepts and notions in clustering, and analysis of their properties
- Analysis and implementation of most important methods for searching for optimal partitions
- Covers different primitives in clustering, such as points, lines, multiple lines, circles, and ellipses
- A new efficient principle of choosing optimal partitions with the most appropriate number of clusters
- Detailed description and analysis of several important applications
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Table of contents (9 chapters)
Keywords
About this book
Suitable for an introductory course on cluster analysis or data mining, with an in-depth mathematical treatment that includes discussions on different measures, primitives (points, lines, etc.) and optimization-based clustering methods, Cluster Analysis and Applications also includes coverage of deep learning based clustering methods.
With clear explanations of ideas and precise definitions of concepts, accompanied by numerous examples and exercises together with Mathematica programs and modules, Cluster Analysis and Applications may be used by students and researchers in various disciplines, working in data analysis or data science.
Authors and Affiliations
About the authors
Kristian Sabo received his Ph.D. in Applied Mathematics from the University of Zagreb in 2007. He works as a Professor at the Department of Mathematics, University of Osijek. His research interests are Applied and Numerical Mathematics (Curve Fitting, Parameter Estimation, Data Cluster Analysis) with applications in Agriculture, Economy, Chemistry, Politics, Electrical Engineering, Medicine, Food Industry, Mechanical Engineering.
Francisco Martínez-Álvarez recevied his Ph.D. in Computer Science from the Pablo de Olavide University in 2010. He works as a Professor at the Department of Computer Science, at the same univeristy. He was the Head of the Department of Computer Science for some years and co-founded the Data Science and Big Data Lab in 2015. He has been a visiting scholar to various universities, such as New York University, Universidad de Chile or Université de Lyon. His research interests include machine learning, optimization, forecasting and big data analytics.
Šime Ungar received his Ph.D. in Topology from the University of Zagreb in 1977. He spent the academic year 1978/79 as a Visiting Assistant Professor at the Department of Mathematics, University of Utah, Salt Lake City, Utah, USA. He worked as a Professor at the Department of Mathematics, University of Zagreb and at the Department of Mathematics, University of Osijek, and is now retired. His research interest is in geometric and algebraic topology, mathematical analysis and inequalities.
Bibliographic Information
Book Title: Cluster Analysis and Applications
Authors: Rudolf Scitovski, Kristian Sabo, Francisco Martínez-Álvarez, Šime Ungar
DOI: https://doi.org/10.1007/978-3-030-74552-3
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021
Hardcover ISBN: 978-3-030-74551-6Published: 23 July 2021
Softcover ISBN: 978-3-030-74554-7Published: 24 July 2022
eBook ISBN: 978-3-030-74552-3Published: 22 July 2021
Edition Number: 1
Number of Pages: X, 271
Number of Illustrations: 7 b/w illustrations, 124 illustrations in colour
Topics: Data Structures and Information Theory, Artificial Intelligence, Theory of Computation, Pattern Recognition, Algorithm Analysis and Problem Complexity, Machine Learning