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Clustering Methodology For Symbolic Data
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Book Synopsis Clustering Methodology for Symbolic Data by : Lynne Billard
Download or read book Clustering Methodology for Symbolic Data written by Lynne Billard and published by John Wiley & Sons. This book was released on 2019-11-04 with total page 348 pages. Available in PDF, EPUB and Kindle. Book excerpt: Covers everything readers need to know about clustering methodology for symbolic data—including new methods and headings—while providing a focus on multi-valued list data, interval data and histogram data This book presents all of the latest developments in the field of clustering methodology for symbolic data—paying special attention to the classification methodology for multi-valued list, interval-valued and histogram-valued data methodology, along with numerous worked examples. The book also offers an expansive discussion of data management techniques showing how to manage the large complex dataset into more manageable datasets ready for analyses. Filled with examples, tables, figures, and case studies, Clustering Methodology for Symbolic Data begins by offering chapters on data management, distance measures, general clustering techniques, partitioning, divisive clustering, and agglomerative and pyramid clustering. Provides new classification methodologies for histogram valued data reaching across many fields in data science Demonstrates how to manage a large complex dataset into manageable datasets ready for analysis Features very large contemporary datasets such as multi-valued list data, interval-valued data, and histogram-valued data Considers classification models by dynamical clustering Features a supporting website hosting relevant data sets Clustering Methodology for Symbolic Data will appeal to practitioners of symbolic data analysis, such as statisticians and economists within the public sectors. It will also be of interest to postgraduate students of, and researchers within, web mining, text mining and bioengineering.
Book Synopsis Clustering Methodology for Symbolic Data by : Lynne Billard
Download or read book Clustering Methodology for Symbolic Data written by Lynne Billard and published by John Wiley & Sons. This book was released on 2019-08-12 with total page 352 pages. Available in PDF, EPUB and Kindle. Book excerpt: Covers everything readers need to know about clustering methodology for symbolic data—including new methods and headings—while providing a focus on multi-valued list data, interval data and histogram data This book presents all of the latest developments in the field of clustering methodology for symbolic data—paying special attention to the classification methodology for multi-valued list, interval-valued and histogram-valued data methodology, along with numerous worked examples. The book also offers an expansive discussion of data management techniques showing how to manage the large complex dataset into more manageable datasets ready for analyses. Filled with examples, tables, figures, and case studies, Clustering Methodology for Symbolic Data begins by offering chapters on data management, distance measures, general clustering techniques, partitioning, divisive clustering, and agglomerative and pyramid clustering. Provides new classification methodologies for histogram valued data reaching across many fields in data science Demonstrates how to manage a large complex dataset into manageable datasets ready for analysis Features very large contemporary datasets such as multi-valued list data, interval-valued data, and histogram-valued data Considers classification models by dynamical clustering Features a supporting website hosting relevant data sets Clustering Methodology for Symbolic Data will appeal to practitioners of symbolic data analysis, such as statisticians and economists within the public sectors. It will also be of interest to postgraduate students of, and researchers within, web mining, text mining and bioengineering.
Book Synopsis Advances in Data Science by : Edwin Diday
Download or read book Advances in Data Science written by Edwin Diday and published by John Wiley & Sons. This book was released on 2020-01-09 with total page 225 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data science unifies statistics, data analysis and machine learning to achieve a better understanding of the masses of data which are produced today, and to improve prediction. Special kinds of data (symbolic, network, complex, compositional) are increasingly frequent in data science. These data require specific methodologies, but there is a lack of reference work in this field. Advances in Data Science fills this gap. It presents a collection of up-to-date contributions by eminent scholars following two international workshops held in Beijing and Paris. The 10 chapters are organized into four parts: Symbolic Data, Complex Data, Network Data and Clustering. They include fundamental contributions, as well as applications to several domains, including business and the social sciences.
Book Synopsis Selected Contributions in Data Analysis and Classification by : Paula Brito
Download or read book Selected Contributions in Data Analysis and Classification written by Paula Brito and published by Springer Science & Business Media. This book was released on 2007-08-27 with total page 619 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume presents recent methodological developments in data analysis and classification. It covers a wide range of topics, including methods for classification and clustering, dissimilarity analysis, consensus methods, conceptual analysis of data, and data mining and knowledge discovery in databases. The book also presents a wide variety of applications, in fields such as biology, micro-array analysis, cyber traffic, and bank fraud detection.
Book Synopsis Analysis of Symbolic Data by : Hans-Hermann Bock
Download or read book Analysis of Symbolic Data written by Hans-Hermann Bock and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 444 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the most recent methods for analyzing and visualizing symbolic data. It generalizes classical methods of exploratory, statistical and graphical data analysis to the case of complex data. Several benchmark examples from National Statistical Offices illustrate the usefulness of the methods. The book contains an extensive bibliography and a subject index.
Book Synopsis Handbook of Cluster Analysis by : Christian Hennig
Download or read book Handbook of Cluster Analysis written by Christian Hennig and published by CRC Press. This book was released on 2015-12-16 with total page 753 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 Synopsis Symbolic Data Analysis by : Lynne Billard
Download or read book Symbolic Data Analysis written by Lynne Billard and published by John Wiley & Sons. This book was released on 2012-05-14 with total page 330 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the advent of computers, very large datasets have become routine. Standard statistical methods don’t have the power or flexibility to analyse these efficiently, and extract the required knowledge. An alternative approach is to summarize a large dataset in such a way that the resulting summary dataset is of a manageable size and yet retains as much of the knowledge in the original dataset as possible. One consequence of this is that the data may no longer be formatted as single values, but be represented by lists, intervals, distributions, etc. The summarized data have their own internal structure, which must be taken into account in any analysis. This text presents a unified account of symbolic data, how they arise, and how they are structured. The reader is introduced to symbolic analytic methods described in the consistent statistical framework required to carry out such a summary and subsequent analysis. Presents a detailed overview of the methods and applications of symbolic data analysis. Includes numerous real examples, taken from a variety of application areas, ranging from health and social sciences, to economics and computing. Features exercises at the end of each chapter, enabling the reader to develop their understanding of the theory. Provides a supplementary website featuring links to download the SODAS software developed exclusively for symbolic data analysis, data sets, and further material. Primarily aimed at statisticians and data analysts, Symbolic Data Analysis is also ideal for scientists working on problems involving large volumes of data from a range of disciplines, including computer science, health and the social sciences. There is also much of use to graduate students of statistical data analysis courses.
Book Synopsis Classification, Clustering, and Data Analysis by : Krzystof Jajuga
Download or read book Classification, Clustering, and Data Analysis written by Krzystof Jajuga and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 468 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book presents a long list of useful methods for classification, clustering and data analysis. By combining theoretical aspects with practical problems, it is designed for researchers as well as for applied statisticians and will support the fast transfer of new methodological advances to a wide range of applications.
Book Synopsis Symbolic Data Analysis and the SODAS Software by : Edwin Diday
Download or read book Symbolic Data Analysis and the SODAS Software written by Edwin Diday and published by John Wiley & Sons. This book was released on 2008-04-15 with total page 476 pages. Available in PDF, EPUB and Kindle. Book excerpt: Symbolic data analysis is a relatively new field that provides a range of methods for analyzing complex datasets. Standard statistical methods do not have the power or flexibility to make sense of very large datasets, and symbolic data analysis techniques have been developed in order to extract knowledge from such data. Symbolic data methods differ from that of data mining, for example, because rather than identifying points of interest in the data, symbolic data methods allow the user to build models of the data and make predictions about future events. This book is the result of the work f a pan-European project team led by Edwin Diday following 3 years work sponsored by EUROSTAT. It includes a full explanation of the new SODAS software developed as a result of this project. The software and methods described highlight the crossover between statistics and computer science, with a particular emphasis on data mining.
Book Synopsis New Developments in Classification and Data Analysis by : Maurizio Vichi
Download or read book New Developments in Classification and Data Analysis written by Maurizio Vichi and published by Springer Science & Business Media. This book was released on 2006-05-06 with total page 372 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume contains revised versions of selected papers presented during the biannual meeting of the Classification and Data Analysis Group of SocietA Italiana di Statistica, which was held in Bologna, September 22-24, 2003. The scientific program of the conference included 80 contributed papers. Moreover it was possible to recruit six internationally renowned invited spe- ers for plenary talks on their current research works regarding the core topics of IFCS (the International Federation of Classification Societies) and Wo- gang Gaul and the colleagues of the GfKl organized a session. Thus, the conference provided a large number of scientists and experts from home and abroad with an attractive forum for discussions and mutual exchange of knowledge. The talks in the different sessions focused on methodological developments in supervised and unsupervised classification and in data analysis, also p- viding relevant contributions in the context of applications. This suggested the presentation of the 43 selected papers in three parts as follows: CLASSIFICATION AND CLUSTERING Non parametric classification Clustering and dissimilarities MULTIVARIATE STATISTICS AND DATA ANALYSIS APPLIED MULTIVARIATE STATISTICS Environmental data Microarray data Behavioural and text data Financial data We wish to express our gratitude to the authors whose enthusiastic p- ticipation made the meeting possible. We are very grateful to the reviewers for the time spent in their professional reviewing work. We would also like to extend our thanks to the chairpersons and discussants of the sessions: their comments and suggestions proved very stimulating both for the authors and the audience.
Book Synopsis Data Clustering: Theory, Algorithms, and Applications, Second Edition by : Guojun Gan
Download or read book Data Clustering: Theory, Algorithms, and Applications, Second Edition written by Guojun Gan and published by SIAM. This book was released on 2020-11-10 with total page 430 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Cluster Analysis for Symbolic Interval Data Using Linear Regression Method by : Fei Liu
Download or read book Cluster Analysis for Symbolic Interval Data Using Linear Regression Method written by Fei Liu and published by . This book was released on 2016 with total page 312 pages. Available in PDF, EPUB and Kindle. Book excerpt: Symbolic data records are becoming a more powerful instrument to deal with large size data sets. Interval-valued data are a special type of symbolic data, for which each observation is a vector of intervals. The typical K-means methods for interval-valued data suppose the data separate to spherical clusters. It usually cannot converge to the correct clusters if the data are not clustering spherically. We propose a K-regressions based clustering method for interval-valued data to recover a more complicated data structure. Assuming the response and predictor variables follow K different linear relationships, the data are initially split into K groups randomly. Then, we apply the new developed symbolic variation" least squares to estimate the parameters of the K symbolic regressions. A data point is then relocated to its closest group in terms of its symbolic distance to the regression lines. This two-step dynamic clustering algorithm continues until the clusters are stable. Further, we introduce an orthogonal regression clustering algorithm (ORCA) for interval-value data to avoid specifying a response variable. Two orthogonal regression methods are proposed: the simple orthogonal regression method and the general orthogonal regression method. We utilize four different methods to determine the optimal number of clusters. Simulation study is conducted to investigate the performance of the ORCA algorithm. We use the Iris data (Fisher, 1936) to test the e effectiveness of the ORCA algorithm.
Book Synopsis Selected Contributions in Data Analysis and Classification by : Paula Brito
Download or read book Selected Contributions in Data Analysis and Classification written by Paula Brito and published by Springer Science & Business Media. This book was released on 2007-08-16 with total page 619 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume presents recent methodological developments in data analysis and classification. It covers a wide range of topics, including methods for classification and clustering, dissimilarity analysis, consensus methods, conceptual analysis of data, and data mining and knowledge discovery in databases. The book also presents a wide variety of applications, in fields such as biology, micro-array analysis, cyber traffic, and bank fraud detection.
Book Synopsis Data Analysis, Classification, and Related Methods by : Henk A.L. Kiers
Download or read book Data Analysis, Classification, and Related Methods written by Henk A.L. Kiers and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 428 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume contains a selection of papers presented at the Seven~h Confer ence of the International Federation of Classification Societies (IFCS-2000), which was held in Namur, Belgium, July 11-14,2000. From the originally sub mitted papers, a careful review process involving two reviewers per paper, led to the selection of 65 papers that were considered suitable for publication in this book. The present book contains original research contributions, innovative ap plications and overview papers in various fields within data analysis, classifi cation, and related methods. Given the fast publication process, the research results are still up-to-date and coincide with their actual presentation at the IFCS-2000 conference. The topics captured are: • Cluster analysis • Comparison of clusterings • Fuzzy clustering • Discriminant analysis • Mixture models • Analysis of relationships data • Symbolic data analysis • Regression trees • Data mining and neural networks • Pattern recognition • Multivariate data analysis • Robust data analysis • Data science and sampling The IFCS (International Federation of Classification Societies) The IFCS promotes the dissemination of technical and scientific information data analysis, classification, related methods, and their applica concerning tions.
Book Synopsis Data Science and Classification by : Vladimir Batagelj
Download or read book Data Science and Classification written by Vladimir Batagelj and published by Springer Science & Business Media. This book was released on 2006-09-05 with total page 350 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Science and Classification provides new methodological developments in data analysis and classification. The broad and comprehensive coverage includes the measurement of similarity and dissimilarity, methods for classification and clustering, network and graph analyses, analysis of symbolic data, and web mining. Beyond structural and theoretical results, the book offers application advice for a variety of problems, in medicine, microarray analysis, social network structures, and music.
Download or read book KI 2006 written by Christian Freksa and published by Springer. This book was released on 2007-08-21 with total page 464 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the thoroughly refereed post-proceedings of the 29th Annual German Conference on Artificial Intelligence, KI 2006, held in Bremen, Germany, in June 2006. This was co-located with RoboCup 2006, the innovative robot soccer world championship, and with ACTUATOR 2006, the 10th International Conference on New Actuators. The 29 revised full papers presented together with two invited contributions were carefully reviewed and selected from 112 submissions.
Book Synopsis Classification, Clustering, and Data Mining Applications by : David Banks
Download or read book Classification, Clustering, and Data Mining Applications written by David Banks and published by Springer Science & Business Media. This book was released on 2011-01-07 with total page 642 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume describes new methods with special emphasis on classification and cluster analysis. These methods are applied to problems in information retrieval, phylogeny, medical diagnosis, microarrays, and other active research areas.
