With the development of Big Data platforms for managing massive amount of data and wide availability of tools for processing these data, the biggest limitation is the lack of trained experts who are qualified to process and interpret the results. This textbook is intended for graduate students and experts using methods of cluster analysis and applications in various fields.
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.
Rudolf Scitovski received his Ph.D. in Applied Mathematics from the University of Zagreb in 1984. He works as a Professor at the Department of Mathematics, University of Osijek. He was the Head of the Department of Mathematics for a long period of time. Before that, he was employed at the Faculty of Electrical Engineering and the Faculty of Economics, University of Osijek. His research interests include least square and least absolute deviations problems, clustering and global optimization.
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.
