Knowledge-Based Clustering PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Knowledge-Based Clustering PDF full book. Access full book title Knowledge-Based Clustering by Witold Pedrycz. Download full books in PDF and EPUB format.
Author: Witold Pedrycz
Publisher: John Wiley & Sons
ISBN: 0471708593
Category : Technology & Engineering
Languages : en
Pages : 336
Get Book Here
Book Description
A comprehensive coverage of emerging and current technology dealing with heterogeneous sources of information, including data, design hints, reinforcement signals from external datasets, and related topics Covers all necessary prerequisites, and if necessary,additional explanations of more advanced topics, to make abstract concepts more tangible Includes illustrative material andwell-known experimentsto offer hands-on experience
Author: Witold Pedrycz
Publisher: John Wiley & Sons
ISBN: 0471708593
Category : Technology & Engineering
Languages : en
Pages : 336
Get Book Here
Book Description
A comprehensive coverage of emerging and current technology dealing with heterogeneous sources of information, including data, design hints, reinforcement signals from external datasets, and related topics Covers all necessary prerequisites, and if necessary,additional explanations of more advanced topics, to make abstract concepts more tangible Includes illustrative material andwell-known experimentsto offer hands-on experience
Author: Charu C. Aggarwal
Publisher: CRC Press
ISBN: 1466558229
Category : Business & Economics
Languages : en
Pages : 648
Get Book Here
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: Oded Maimon
Publisher: Springer Science & Business Media
ISBN: 038725465X
Category : Computers
Languages : en
Pages : 1378
Get Book Here
Book Description
Data Mining and Knowledge Discovery Handbook organizes all major concepts, theories, methodologies, trends, challenges and applications of data mining (DM) and knowledge discovery in databases (KDD) into a coherent and unified repository. This book first surveys, then provides comprehensive yet concise algorithmic descriptions of methods, including classic methods plus the extensions and novel methods developed recently. This volume concludes with in-depth descriptions of data mining applications in various interdisciplinary industries including finance, marketing, medicine, biology, engineering, telecommunications, software, and security. Data Mining and Knowledge Discovery Handbook is designed for research scientists and graduate-level students in computer science and engineering. This book is also suitable for professionals in fields such as computing applications, information systems management, and strategic research management.
Author: Charles Bouveyron
Publisher: Cambridge University Press
ISBN: 1108640591
Category : Mathematics
Languages : en
Pages : 447
Get Book Here
Book Description
Cluster analysis finds groups in data automatically. Most methods have been heuristic and leave open such central questions as: how many clusters are there? Which method should I use? How should I handle outliers? Classification assigns new observations to groups given previously classified observations, and also has open questions about parameter tuning, robustness and uncertainty assessment. This book frames cluster analysis and classification in terms of statistical models, thus yielding principled estimation, testing and prediction methods, and sound answers to the central questions. It builds the basic ideas in an accessible but rigorous way, with extensive data examples and R code; describes modern approaches to high-dimensional data and networks; and explains such recent advances as Bayesian regularization, non-Gaussian model-based clustering, cluster merging, variable selection, semi-supervised and robust classification, clustering of functional data, text and images, and co-clustering. Written for advanced undergraduates in data science, as well as researchers and practitioners, it assumes basic knowledge of multivariate calculus, linear algebra, probability and statistics.
Author: Michael R. Anderberg
Publisher: Academic Press
ISBN: 1483191397
Category : Mathematics
Languages : en
Pages : 376
Get Book Here
Book Description
Cluster Analysis for Applications deals with methods and various applications of cluster analysis. Topics covered range from variables and scales to measures of association among variables and among data units. Conceptual problems in cluster analysis are discussed, along with hierarchical and non-hierarchical clustering methods. The necessary elements of data analysis, statistics, cluster analysis, and computer implementation are integrated vertically to cover the complete path from raw data to a finished analysis. Comprised of 10 chapters, this book begins with an introduction to the subject of cluster analysis and its uses as well as category sorting problems and the need for cluster analysis algorithms. The next three chapters give a detailed account of variables and association measures, with emphasis on strategies for dealing with problems containing variables of mixed types. Subsequent chapters focus on the central techniques of cluster analysis with particular reference to computational considerations; interpretation of clustering results; and techniques and strategies for making the most effective use of cluster analysis. The final chapter suggests an approach for the evaluation of alternative clustering methods. The presentation is capped with a complete set of implementing computer programs listed in the Appendices to make the use of cluster analysis as painless and free of mechanical error as is possible. This monograph is intended for students and workers who have encountered the notion of cluster analysis.
Author: A. Srivastava
Publisher:
ISBN:
Category :
Languages : en
Pages :
Get Book Here
Book Description
Author: Charu C. Aggarwal
Publisher: CRC Press
ISBN: 1315360411
Category : Business & Economics
Languages : en
Pages : 654
Get Book Here
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: Jianhong Xue
Publisher:
ISBN:
Category : Industrial clusters
Languages : en
Pages : 194
Get Book Here
Book Description
Although an increasing body of literatures has focused on explaining the driving forces of industry clusters, the underlying causes of a knowledge-based cluster are still unclear. This research takes factors that impact innovative production as the key to understand the driving force of a knowledge-based cluster. The study argues that regional innovative production depends not only on the sizes of human capital and knowledge pools, but also on the speed and relevancy rate that human capital and knowledge combine. For empirical testing, an approach of structural equations with latent variable is employed under the case of biotechnology industry in the U.S. The results of the empirical model support such a hypothesis, suggesting that factors that increase the sizes of human capital and knowledge pools as well as the speed and relevancy rate that human capital and knowledge capital combine are critical to regional innovative production, and thus, the clustering of a knowledge-based industry. Hence, it has important implications for devising appropriate regional development policy and business strategy.
Author: Silvia Maria Bruno
Publisher:
ISBN:
Category : Expert systems (Computer science)
Languages : en
Pages : 168
Get Book Here
Book Description
Author: John F. Wilson
Publisher: Routledge
ISBN: 1000609286
Category : Business & Economics
Languages : en
Pages : 321
Get Book Here
Book Description
Industrial Clusters shows the latest state of knowledge on the topic of industrial clusters, with a particular focus on clustering in the UK, bringing together a chronological coverage of the phenomenon. This set of original essays by a group of leading business and industrial historians offers fresh perspectives about clusters and clustering. A primary emphasis of the collection is how knowledge is generated and disseminated across a cluster, and whether these processes stimulated innovation and consequently longer-term sustainability. This analysis also prompts questions about which unit of analysis to examine, from the entrepreneurs and firms they created through to the industry as a whole and district in which they are located, or whether one should look outside the region for explanatory factors. Covering regions as diverse as North Wales, the Scottish Highlands, the City of London, the Potteries, Sheffield and Lancashire, the essays have been channelled to provide a detailed understanding of these issues. The editors have also provided a challenging Conclusion that suggests a new research agenda that could well unravel some of the mysteries associated with clustering. This edited collection will be of interest to international researchers, academics and students in the fields of business and management history, innovation, industrialisation and clusters.
