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Author: Rui Xu
Publisher: John Wiley & Sons
ISBN: 0470382783
Category : Mathematics
Languages : en
Pages : 400
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Book Description
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.
Author: Rui Xu
Publisher: John Wiley & Sons
ISBN: 0470382783
Category : Mathematics
Languages : en
Pages : 400
Get Book Here
Book Description
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.
Author: Paolo Giordani
Publisher: Springer Nature
ISBN: 9811305536
Category : Mathematics
Languages : en
Pages : 346
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Book Description
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.
Author: Charu C. Aggarwal
Publisher: CRC Press
ISBN: 1466558229
Category : Business & Economics
Languages : en
Pages : 648
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Book Description
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.
Author: Sugato Basu
Publisher: CRC Press
ISBN: 9781584889977
Category : Computers
Languages : en
Pages : 472
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Book Description
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.
Author: M. Korolija
Publisher: World Scientific
ISBN: 9789810242336
Category : Science
Languages : en
Pages : 476
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Book Description
In the past three decades our understanding of the clustering behavior of nucleons in both nuclear structure and nuclear dynamics has evolved considerably. Moreover, the notion of the cluster has made its way into a number of scientific disciplines. This book provides an overview of the current understanding of clustering phenomena in nuclear structure and nuclear dynamics. The topics covered include: fundamental aspects of nuclear clustering, models of nucleon clusterization, clustering aspects of nuclear structure, selected topics on clustering aspects in medium- and high-energy nucleus-nucleus collisions.
Author: Jeeva Jose
Publisher: Anchor Academic Publishing
ISBN: 3960676042
Category : Computers
Languages : en
Pages : 71
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Book Description
With the opening of the Indian economy, many multinational corporations are shifting their manufacturing base to India. This includes setting up green field projects or acquiring established business firms of India. The region of this business unit is expanding globally. The variety and size of the customer base is expanding and the business risk related to bad debts is increasing. Close monitoring and analysis of payment trends helps to predict customer behavior and predict the chances of customer financial strength. The present manufacturing companies generate and store tremendous amount of data. The amount of data is so huge that manual analysis of the data is difficult. This creates a great demand for data mining to extract useful information buried within these data sets. One of the major concerns that affect companies’ investments and profitability is bad debts; this can be reduced by identifying past customer behavior and reaching the suitable payment terms. The Clustering and Prediction module was implemented in WEKA – a free open source software written in Java. This study model can be extended to the development of a general purpose software package to predict payment trends of customers in any organisation.
Author: Guojun Gan
Publisher: SIAM
ISBN: 1611976332
Category : Mathematics
Languages : en
Pages : 430
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Book Description
Data clustering, also known as cluster analysis, is an unsupervised process that divides a set of objects into homogeneous groups. Since the publication of the first edition of this monograph in 2007, development in the area has exploded, especially in clustering algorithms for big data and open-source software for cluster analysis. This second edition reflects these new developments, covers the basics of data clustering, includes a list of popular clustering algorithms, and provides program code that helps users implement clustering algorithms. Data Clustering: Theory, Algorithms and Applications, Second Edition will be of interest to researchers, practitioners, and data scientists as well as undergraduate and graduate students.
Author: Alex Davies
Publisher: Sams Publishing
ISBN: 0672332817
Category : Computers
Languages : en
Pages : 218
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Book Description
Clustering is the final component of MySQL that makes it truly enterprise-level and able to compete fully with proprietary databases such as Oracle and Microsoft SQL Server. The increased number of high-demand, high-productivity corporations and institutions choosing MySQL, including MIT, the Department of Homeland Security, NASA, and Nokia, to name a few, need the benefit of clustering databases for high performance and scalability. MySQL AB has stepped up to the plate to offer advanced, high-availability, reliable clustering. As this demand has increased, so has the need for information. MySQL Clustering offers thorough, authoritative instruction on setting up and administering a MySQL Cluster from the developers of the cluster itself. You will learn about everything from installation and configuration to performance and troubleshooting in this authoritative reference guide to MySQL clustering.
Author: Sadaaki Miyamoto
Publisher: Springer Science & Business Media
ISBN: 3540787364
Category : Computers
Languages : en
Pages : 252
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Book Description
Recently many researchers are working on cluster analysis as a main tool for exploratory data analysis and data mining. A notable feature is that specialists in di?erent ?elds of sciences are considering the tool of data clustering to be useful. A major reason is that clustering algorithms and software are ?exible in thesensethatdi?erentmathematicalframeworksareemployedinthealgorithms and a user can select a suitable method according to his application. Moreover clusteringalgorithmshavedi?erentoutputsrangingfromtheolddendrogramsof agglomerativeclustering to more recent self-organizingmaps. Thus, a researcher or user can choose an appropriate output suited to his purpose,which is another ?exibility of the methods of clustering. An old and still most popular method is the K-means which use K cluster centers. A group of data is gathered around a cluster center and thus forms a cluster. The main subject of this book is the fuzzy c-means proposed by Dunn and Bezdek and their variations including recent studies. A main reasonwhy we concentrate on fuzzy c-means is that most methodology and application studies infuzzy clusteringusefuzzy c-means,andfuzzy c-meansshouldbe consideredto beamajortechniqueofclusteringingeneral,regardlesswhetheroneisinterested in fuzzy methods or not. Moreover recent advances in clustering techniques are rapid and we requirea new textbook that includes recent algorithms.We should also note that several books have recently been published but the contents do not include some methods studied herein.
Author: Slawomir Wierzchoń
Publisher: Springer
ISBN: 3319693085
Category : Technology & Engineering
Languages : en
Pages : 433
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Book Description
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 formal concepts is particularly vital in the epoch of Big Data; due to the volume and characteristics of the data, it is no longer feasible to predominantly rely on merely viewing the data when facing a clustering problem. Usually clustering involves choosing similar objects and grouping them together. To facilitate the choice of similarity measures for complex and big data, various measures of object similarity, based on quantitative (like numerical measurement results) and qualitative features (like text), as well as combinations of the two, are described, as well as graph-based similarity measures for (hyper) linked objects and measures for multilayered graphs. Numerous variants demonstrating how such similarity measures can be exploited when defining clustering cost functions are also presented. In addition, the book provides an overview of approaches to handling large collections of objects in a reasonable time. In particular, it addresses grid-based methods, sampling methods, parallelization via Map-Reduce, usage of tree-structures, random projections and various heuristic approaches, especially those used for community detection.
