Blackwell, 2007. — 303 p. — ISBN: 978-3-7643-7987-2. Data clustering is a common technique for statistical data analysis, which is used in many fields, including machine learning, data mining, pattern recognition, image analysis and bioinformatics. Clustering is the classification of similar objects into different groups, or more precisely, the partitioning of a data set into...
Academic Press, 1973. P. 360. (Probability and mathematical statistics) Originally presented as the author's thesis, University of Texas at Austin, 1971.
Gistrup: River Publishers, 2021. — 518 p. List of Algorithms. List of Examples. List of Lemmas and Theorems. List of Video Links. The Art and Science of Clustering. Clusters: The Human Point of View (HPOV). What are Clusters? Exercises. Uncertainty: Fuzzy Sets and Models. Fuzzy Sets and Models. Fuzziness and Probability. Exercises. Clusters: The Computer Point of View (CPOV)....
Gistrup: River Publishers, 2021. — 518 p. List of Algorithms List of Examples List of Lemmas and Theorems List of Video Links The Art and Science of Clustering Clusters: The Human Point of View (HPOV) What are Clusters? Exercises Uncertainty: Fuzzy Sets and Models Fuzzy Sets and Models Fuzziness and Probability Exercises Clusters: The Computer Point of View (CPOV) Label Vectors...
Nijgh-Wolters-Noordhoff Universitaire, 1973. — 122 p. This book contains a survey of a number of techniques of clustering analysis. The merits and demerits of the procedures described are also discussed so that the research worker can make an informed choice between them. These techniques have been published in a very great number of journals which are not all easily accessible...
Benjamin S. Duran Cluster Analysis — Lecture notes in economics and mathematical systems ; 100 : Econometrics // Berlin, Heidelberg, Springer, 1974. A tremendous amount of work has been done over the last thirty years in cluster analysis, with a significant amount occurring since 1960. A substantial portion of this work has appeared in many journals, including numerous applied...
Springer, 1974. — 146 p. A tremendous amount of work has been done over the last thirty years in cluster analysis, with a significant amount occurring since 1960. A substantial portion of this work has appeared in many journals, including numerous applied journals, and a unified exposition is lacking. The purpose of this monograph is to supply such an exposition by presenting a...
5th Edition. — John Wiley & Sons, 2011. — 360 p. — ISBN: 978-0-470-74991-3. Cluster analysis comprises a range of methods for classifying multivariate data into subgroups. By organizing multivariate data into such subgroups, clustering can help reveal the characteristics of any structure or patterns present. These techniques have proven useful in a wide range of areas such as...
Boca Raton: Chapman & Hall/CRC, 2016. — 753 p. Optimization methods Dissimilarity-based methods Methods based on probability models Methods based on density modes and level sets Specific cluster and data formats Cluster validation and further general issues.
CRC, 2015. — 773 p. Handbook of Cluster Analysis provides a comprehensive and unified account of the main research developments in cluster analysis. Written by active, distinguished researchers in this area, the book helps readers make informed choices of the most suitable clustering approach for their problem and make better use of existing cluster analysis tools. The book is...
Monograph. West Sussex, England. Publisher: John Wiley & Sons, LTD, 288 p. Language: English. Fuzzy Cluster Analysis presents advanced and powerful fuzzy clustering techniques. This thorough and self-contained introduction to fuzzy clustering methods and applications covers classification, image recognition, data analysis and rule generation. Combining theoretical and practical...
Sussex" Wiley, 1999. - 289 p. When writing this book, our intention was to give a self-contained and methodical introduction to fuzzy cluster analysis with its areas of application and to provide a systematic description of different fuzzy clustering techniques, from which the user can choose the methods appropriate for his problem. The book applies to computer scientists,...
Hoboken, New Jersey: John Wiley & Sons, Inc., 2005. – 355 p. Current statistical software packages contain only a few clustering techniques and they are not the more modern methods. This forced us to write new programs, the use of which is described in the book. Their present version is for IBM PC and compatible machines, but the source codes are very portable and have run on...
Dulles: Mercury Learning and Information, 2015. — 300 p. Cluster analysis is used in data mining and is a common technique for statistical data analysis used in many fields of study, such as the medical & life sciences, behavioral & social sciences, engineering, and in computer science. Designed for training industry professionals or for a course on clustering and...
CRC Press. Taylor & Francis Group, 2014. — 392 p. — ISBN: 1439857962. Clustering remains a vibrant area of research in statistics. Although there are many books on this topic, there are relatively few that are well founded in the theoretical aspects. In Robust Cluster Analysis and Variable Selection , Gunter Ritter presents an overview of the theory and applications of...
N.-Y.: Chapman and Hall/CRC, 2014. - 392 p. Clustering remains a vibrant area of research in statistics. Although there are many books on this topic, there are relatively few that are well founded in the theoretical aspects. In Robust Cluster Analysis and Variable Selection, Gunter Ritter presents an overview of the theory and applications of probabilistic clustering and...
Lifetime Learning Publications, 1984. — 350 p. This book explains and illustrates the most frequently used methods of hierarchical cluster analysis so that they can be understood and practiced by researchers with limited backgrounds in mathematics and statistics. Widely applicable inresearch, these methods are used to determine clusters of similar objects. For example,...
Lifetime Learning Publications, 1984. — 350 p. This book explains and illustrates the most frequently used methods of hierarchical cluster analysis so that they can be understood and practiced by researchers with limited backgrounds in mathematics and statistics. Widely applicable inresearch, these methods are used to determine clusters of similar objects. For example,...
Boca Raton: CRC Press, 2025. — 222 p. Classification problems are common in business, medicine, science, engineering, and other sectors of the economy. Data scientists and Machine Learning professionals solve these problems through the use of classifiers. Choosing one of these data-driven classification algorithms for a given problem is a challenging task. An important aspect...
Springer, 2021. — 277 p. — ISBN: 978-3-030-74551-6. With the development of Big Data platforms for managing the massive amounts of data and the 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...
Springer, 2018. — 433 p. — (Studies in Big Data 34). — ISBN: 978-3-319-69307-1. This book provides the reader with a basic understanding of the formal concepts of the cluster, clustering, partition, cluster analysis etc. The book explains feature-based, graph-based and spectral clustering methods and discusses their formal similarities and differences. Understanding the related...
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