Grouping Multidimensional Data
Author: Jacob Kogan
Publisher: Taylor & Francis
Published: 2006-02-10
Total Pages: 296
ISBN-13: 9783540283485
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Author: Jacob Kogan
Publisher: Taylor & Francis
Published: 2006-02-10
Total Pages: 296
ISBN-13: 9783540283485
DOWNLOAD EBOOKPublisher description
Author: R. Hamerslag
Publisher:
Published: 1987
Total Pages: 44
ISBN-13:
DOWNLOAD EBOOKAuthor: Jacob Kogan
Publisher: Cambridge University Press
Published: 2007
Total Pages: 228
ISBN-13: 9780521617932
DOWNLOAD EBOOKFocuses on a few of the important clustering algorithms in the context of information retrieval.
Author: Rafanelli, Maurizio
Publisher: IGI Global
Published: 2002-07-01
Total Pages: 340
ISBN-13: 1591400864
DOWNLOAD EBOOKMultidimensional Databases: Problems and Solutions strives to be the point of reference for the most important issues in the field of multidimensional databases. This book provides a brief history of the field and distinguishes between what is new in recent research and what is merely a renaming of old concepts. In addition Multidimensional Databases: Problems and Solutions outlines the incredible advances in technology and ever increasing demands from users in the most diverse applicative areas such as finance, medicine, statistics, business, and many more. Many of the most distinguished and well-known researchers have contributed to this book writing about their own specific field.
Author: Bernard Fichet
Publisher: Springer Science & Business Media
Published: 2011-03-04
Total Pages: 460
ISBN-13: 3642133126
DOWNLOAD EBOOKThe growing capabilities in generating and collecting data has risen an urgent need of new techniques and tools in order to analyze, classify and summarize statistical information, as well as to discover and characterize trends, and to automatically bag anomalies. This volume provides the latest advances in data analysis methods for multidimensional data which can present a complex structure: The book offers a selection of papers presented at the first Joint Meeting of the Société Francophone de Classification and the Classification and Data Analysis Group of the Italian Statistical Society. Special attention is paid to new methodological contributions from both the theoretical and the applicative point of views, in the fields of Clustering, Classification, Time Series Analysis, Multidimensional Data Analysis, Knowledge Discovery from Large Datasets, Spatial Statistics.
Author: John Paredes
Publisher: John Paredes
Published: 2009
Total Pages: 354
ISBN-13: 0981775306
DOWNLOAD EBOOKThe Multi-dimensional Data Modeling Toolkit represents over 15 years of hands-on experience developing multidimensional analytic applications for over a dozen companies in a variety of application areas. Written in a tutorial style, this book gives, in plain English, a step-by-step development of the defining principles of OLAP analysis through the lens of the programming language at the heart of Oracle's OLAP database option. You will find this book packed with examples, tricks and techniques, concrete illustrations of the programming elements needed to implement. The basics will all be there as well as advanced techniques that you can use to address the most demanding requirements. OLAP will be addressed as an analysis platform. You will learn how to make business intelligence applications smarter by upping the analytical octane. You will learn both the classic applications of OLAP analysis as well as more exotic approaches. You will learn where OLAP fits in among other analytical approaches such as statistics and data mining. So whether you are a developer wanting to learn Oracle's counterpart to Microsoft's MDX, or an analyst wanting to understand the quantitative possibilities of OLAP, The Multi-dimensional Data Modeling Toolkit will show you what you need to know to go from beginner to expert in the application of OLAP analytics with Oracle OLAP DML.
Author: Bhattacharyya, Siddhartha
Publisher: IGI Global
Published: 2016-11-29
Total Pages: 471
ISBN-13: 1522517774
DOWNLOAD EBOOKData mining analysis techniques have undergone significant developments in recent years. This has led to improved uses throughout numerous functions and applications. Intelligent Multidimensional Data Clustering and Analysis is an authoritative reference source for the latest scholarly research on the advantages and challenges presented by the use of cluster analysis techniques. Highlighting theoretical foundations, computing paradigms, and real-world applications, this book is ideally designed for researchers, practitioners, upper-level students, and professionals interested in the latest developments in cluster analysis for large data sets.
Author: Guojun Gan
Publisher: SIAM
Published: 2020-11-10
Total Pages: 430
ISBN-13: 1611976332
DOWNLOAD EBOOKData 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: Leonard Kaufman
Publisher: John Wiley & Sons
Published: 2009-09-25
Total Pages: 368
ISBN-13: 0470317485
DOWNLOAD EBOOKThe Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists. "Cluster analysis is the increasingly important and practical subject of finding groupings in data. The authors set out to write a book for the user who does not necessarily have an extensive background in mathematics. They succeed very well." —Mathematical Reviews "Finding Groups in Data [is] a clear, readable, and interesting presentation of a small number of clustering methods. In addition, the book introduced some interesting innovations of applied value to clustering literature." —Journal of Classification "This is a very good, easy-to-read, and practical book. It has many nice features and is highly recommended for students and practitioners in various fields of study." —Technometrics An introduction to the practical application of cluster analysis, this text presents a selection of methods that together can deal with most applications. These methods are chosen for their robustness, consistency, and general applicability. This book discusses various types of data, including interval-scaled and binary variables as well as similarity data, and explains how these can be transformed prior to clustering.
Author: Christian Jensen
Publisher: Springer Nature
Published: 2022-05-31
Total Pages: 95
ISBN-13: 3031018419
DOWNLOAD EBOOKThe present book's subject is multidimensional data models and data modeling concepts as they are applied in real data warehouses. The book aims to present the most important concepts within this subject in a precise and understandable manner. The book's coverage of fundamental concepts includes data cubes and their elements, such as dimensions, facts, and measures and their representation in a relational setting; it includes architecture-related concepts; and it includes the querying of multidimensional databases. The book also covers advanced multidimensional concepts that are considered to be particularly important. This coverage includes advanced dimension-related concepts such as slowly changing dimensions, degenerate and junk dimensions, outriggers, parent-child hierarchies, and unbalanced, non-covering, and non-strict hierarchies. The book offers a principled overview of key implementation techniques that are particularly important to multidimensional databases, including materialized views, bitmap indices, join indices, and star join processing. The book ends with a chapter that presents the literature on which the book is based and offers further readings for those readers who wish to engage in more in-depth study of specific aspects of the book's subject. Table of Contents: Introduction / Fundamental Concepts / Advanced Concepts / Implementation Issues / Further Readings