Clustering
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Algorithms For Clustering Data
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Author : Anil K. Jain
language : en
Publisher:
Release Date : 1988
Algorithms For Clustering Data written by Anil K. Jain and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1988 with Computers categories.
An Introduction To Clustering With R
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Author : Paolo Giordani
language : en
Publisher: Springer Nature
Release Date : 2020-08-27
An Introduction To Clustering With R written by Paolo Giordani and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-08-27 with Mathematics categories.
The purpose of this book is to thoroughly prepare the reader for applied research in clustering. Cluster analysis comprises a class of statistical techniques for classifying multivariate data into groups or clusters based on their similar features. Clustering is nowadays widely used in several domains of research, such as social sciences, psychology, and marketing, highlighting its multidisciplinary nature. This book provides an accessible and comprehensive introduction to clustering and offers practical guidelines for applying clustering tools by carefully chosen real-life datasets and extensive data analyses. The procedures addressed in this book include traditional hard clustering methods and up-to-date developments in soft clustering. Attention is paid to practical examples and applications through the open source statistical software R. Commented R code and output for conducting, step by step, complete cluster analyses are available. The book is intended for researchers interested in applying clustering methods. Basic notions on theoretical issues and on R are provided so that professionals as well as novices with little or no background in the subject will benefit from the book.
Constrained Clustering
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Author : Sugato Basu
language : en
Publisher: CRC Press
Release Date : 2008-08-18
Constrained Clustering written by Sugato Basu and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-08-18 with Computers categories.
Since the initial work on constrained clustering, there have been numerous advances in methods, applications, and our understanding of the theoretical properties of constraints and constrained clustering algorithms. Bringing these developments together, Constrained Clustering: Advances in Algorithms, Theory, and Applications presents an extensive collection of the latest innovations in clustering data analysis methods that use background knowledge encoded as constraints. Algorithms The first five chapters of this volume investigate advances in the use of instance-level, pairwise constraints for partitional and hierarchical clustering. The book then explores other types of constraints for clustering, including cluster size balancing, minimum cluster size,and cluster-level relational constraints. Theory It also describes variations of the traditional clustering under constraints problem as well as approximation algorithms with helpful performance guarantees. Applications The book ends by applying clustering with constraints to relational data, privacy-preserving data publishing, and video surveillance data. It discusses an interactive visual clustering approach, a distance metric learning approach, existential constraints, and automatically generated constraints. With contributions from industrial researchers and leading academic experts who pioneered the field, this volume delivers thorough coverage of the capabilities and limitations of constrained clustering methods as well as introduces new types of constraints and clustering algorithms.
Clustering
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Author : Boris Mirkin
language : en
Publisher: CRC Press
Release Date : 2012-10-17
Clustering written by Boris Mirkin and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-10-17 with Business & Economics categories.
Often considered more of an art than a science, books on clustering have been dominated by learning through example with techniques chosen almost through trial and error. Even the two most popular, and most related, clustering methods—K-Means for partitioning and Ward's method for hierarchical clustering—have lacked the theoretical underpinning required to establish a firm relationship between the two methods and relevant interpretation aids. Other approaches, such as spectral clustering or consensus clustering, are considered absolutely unrelated to each other or to the two above mentioned methods. Clustering: A Data Recovery Approach, Second Edition presents a unified modeling approach for the most popular clustering methods: the K-Means and hierarchical techniques, especially for divisive clustering. It significantly expands coverage of the mathematics of data recovery, and includes a new chapter covering more recent popular network clustering approaches—spectral, modularity and uniform, additive, and consensus—treated within the same data recovery approach. Another added chapter covers cluster validation and interpretation, including recent developments for ontology-driven interpretation of clusters. Altogether, the insertions added a hundred pages to the book, even in spite of the fact that fragments unrelated to the main topics were removed. Illustrated using a set of small real-world datasets and more than a hundred examples, the book is oriented towards students, practitioners, and theoreticians of cluster analysis. Covering topics that are beyond the scope of most texts, the author’s explanations of data recovery methods, theory-based advice, pre- and post-processing issues and his clear, practical instructions for real-world data mining make this book ideally suited for teaching, self-study, and professional reference.
Data Science
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Author : Francesco Palumbo
language : en
Publisher: Springer
Release Date : 2017-07-04
Data Science written by Francesco Palumbo and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-07-04 with Mathematics categories.
This edited volume on the latest advances in data science covers a wide range of topics in the context of data analysis and classification. In particular, it includes contributions on classification methods for high-dimensional data, clustering methods, multivariate statistical methods, and various applications. The book gathers a selection of peer-reviewed contributions presented at the Fifteenth Conference of the International Federation of Classification Societies (IFCS2015), which was hosted by the Alma Mater Studiorum, University of Bologna, from July 5 to 8, 2015.
Data Clustering
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Author : Charu C. Aggarwal
language : en
Publisher: CRC Press
Release Date : 2013-08-21
Data Clustering written by Charu C. Aggarwal and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-08-21 with Business & Economics categories.
Research on the problem of clustering tends to be fragmented across the pattern recognition, database, data mining, and machine learning communities. Addressing this problem in a unified way, Data Clustering: Algorithms and Applications provides complete coverage of the entire area of clustering, from basic methods to more refined and complex data clustering approaches. It pays special attention to recent issues in graphs, social networks, and other domains. The book focuses on three primary aspects of data clustering: Methods, describing key techniques commonly used for clustering, such as feature selection, agglomerative clustering, partitional clustering, density-based clustering, probabilistic clustering, grid-based clustering, spectral clustering, and nonnegative matrix factorization Domains, covering methods used for different domains of data, such as categorical data, text data, multimedia data, graph data, biological data, stream data, uncertain data, time series clustering, high-dimensional clustering, and big data Variations and Insights, discussing important variations of the clustering process, such as semisupervised clustering, interactive clustering, multiview clustering, cluster ensembles, and cluster validation In this book, top researchers from around the world explore the characteristics of clustering problems in a variety of application areas. They also explain how to glean detailed insight from the clustering process—including how to verify the quality of the underlying clusters—through supervision, human intervention, or the automated generation of alternative clusters.
Generalized Density Based Clustering For Spatial Data Mining
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Author : Jörg Sander
language : en
Publisher: Herbert Utz Verlag
Release Date : 1999
Generalized Density Based Clustering For Spatial Data Mining written by Jörg Sander and has been published by Herbert Utz Verlag this book supported file pdf, txt, epub, kindle and other format this book has been release on 1999 with categories.
Clustering
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Author : Rui Xu
language : en
Publisher: John Wiley & Sons
Release Date : 2008-11-03
Clustering written by Rui Xu and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-11-03 with Mathematics categories.
This is the first book to take a truly comprehensive look at clustering. It begins with an introduction to cluster analysis and goes on to explore: proximity measures; hierarchical clustering; partition clustering; neural network-based clustering; kernel-based clustering; sequential data clustering; large-scale data clustering; data visualization and high-dimensional data clustering; and cluster validation. The authors assume no previous background in clustering and their generous inclusion of examples and references help make the subject matter comprehensible for readers of varying levels and backgrounds.
Clustering And Information Retrieval
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Author : Weili Wu
language : en
Publisher: Springer Science & Business Media
Release Date : 2003-11-30
Clustering And Information Retrieval written by Weili Wu and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-11-30 with Computers categories.
Clustering is an important technique for discovering relatively dense sub-regions or sub-spaces of a multi-dimension data distribution. Clus tering has been used in information retrieval for many different purposes, such as query expansion, document grouping, document indexing, and visualization of search results. In this book, we address issues of cluster ing algorithms, evaluation methodologies, applications, and architectures for information retrieval. The first two chapters discuss clustering algorithms. The chapter from Baeza-Yates et al. describes a clustering method for a general metric space which is a common model of data relevant to information retrieval. The chapter by Guha, Rastogi, and Shim presents a survey as well as detailed discussion of two clustering algorithms: CURE and ROCK for numeric data and categorical data respectively. Evaluation methodologies are addressed in the next two chapters. Ertoz et al. demonstrate the use of text retrieval benchmarks, such as TRECS, to evaluate clustering algorithms. He et al. provide objective measures of clustering quality in their chapter. Applications of clustering methods to information retrieval is ad dressed in the next four chapters. Chu et al. and Noel et al. explore feature selection using word stems, phrases, and link associations for document clustering and indexing. Wen et al. and Sung et al. discuss applications of clustering to user queries and data cleansing. Finally, we consider the problem of designing architectures for infor mation retrieval. Crichton, Hughes, and Kelly elaborate on the devel opment of a scientific data system architecture for information retrieval.
Robust Cluster Analysis And Variable Selection
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Author : Gunter Ritter
language : en
Publisher: CRC Press
Release Date : 2014-09-02
Robust Cluster Analysis And Variable Selection written by Gunter Ritter and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-09-02 with Computers categories.
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 variable selection, synthesizing the key research results of the last 50 years. The author focuses on the robust clustering methods he found to be the most useful on simulated data and real-time applications. The book provides clear guidance for the varying needs of both applications, describing scenarios in which accuracy and speed are the primary goals. Robust Cluster Analysis and Variable Selection includes all of the important theoretical details, and covers the key probabilistic models, robustness issues, optimization algorithms, validation techniques, and variable selection methods. The book illustrates the different methods with simulated data and applies them to real-world data sets that can be easily downloaded from the web. This provides you with guidance in how to use clustering methods as well as applicable procedures and algorithms without having to understand their probabilistic fundamentals.