majedur rahman and chowdhury quamruzzaman a proposal of clay mine based on geological data

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William Ashcroft A Petroleum Geologist's Guide to Seismic Reflection

This book is written for advanced earth science students, geologists, petroleum engineers and others who want to get quickly ‘up to speed’ on the interpretation of reflection seismic data. It is a development of material given to students on the MSc course in Petroleum Geology at Aberdeen University and takes the form of a course manual rather than a systematic textbook. It can be used as a self-contained course for individual study, or as the basis for a class programme. The book clarifies those aspects of the subject that students tend to find difficult, and provides insights through practical tutorials which aim to reinforce and deepen understanding of key topics and provide the reader with a measure of feedback on progress. Some tutorials may only involve drawing simple diagrams, but many are computer-aided (PC based) with graphics output to give insight into key steps in seismic data processing or into the seismic response of some common geological scenarios. Part I of the book covers basic ideas and it ends with two tutorials in 2-D structural interpretation. Part II concentrates on the current seismic reflection contribution to reservoir studies, based on 3-D data.

13397.59 рублей

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Mohamed Nadif Co-Clustering. Models, Algorithms and Applications

Cluster or co-cluster analyses are important tools in a variety of scientific areas. The introduction of this book presents a state of the art of already well-established, as well as more recent methods of co-clustering. The authors mainly deal with the two-mode partitioning under different approaches, but pay particular attention to a probabilistic approach. Chapter 1 concerns clustering in general and the model-based clustering in particular. The authors briefly review the classical clustering methods and focus on the mixture model. They present and discuss the use of different mixtures adapted to different types of data. The algorithms used are described and related works with different classical methods are presented and commented upon. This chapter is useful in tackling the problem of co-clustering under the mixture approach. Chapter 2 is devoted to the latent block model proposed in the mixture approach context. The authors discuss this model in detail and present its interest regarding co-clustering. Various algorithms are presented in a general context. Chapter 3 focuses on binary and categorical data. It presents, in detail, the appropriated latent block mixture models. Variants of these models and algorithms are presented and illustrated using examples. Chapter 4 focuses on contingency data. Mutual information, phi-squared and model-based co-clustering are studied. Models, algorithms and connections among different approaches are described and illustrated. Chapter 5 presents the case of continuous data. In the same way, the different approaches used in the previous chapters are extended to this situation. Contents 1. Cluster Analysis. 2. Model-Based Co-Clustering. 3. Co-Clustering of Binary and Categorical Data. 4. Co-Clustering of Contingency Tables. 5. Co-Clustering of Continuous Data. About the Authors Gérard Govaert is Professor at the University of Technology of Compiègne, France. He is also a member of the CNRS Laboratory Heudiasyc (Heuristic and diagnostic of complex systems). His research interests include latent structure modeling, model selection, model-based cluster analysis, block clustering and statistical pattern recognition. He is one of the authors of the MIXMOD (MIXtureMODelling) software. Mohamed Nadif is Professor at the University of Paris-Descartes, France, where he is a member of LIPADE (Paris Descartes computer science laboratory) in the Mathematics and Computer Science department. His research interests include machine learning, data mining, model-based cluster analysis, co-clustering, factorization and data analysis. Cluster Analysis is an important tool in a variety of scientific areas. Chapter 1 briefly presents a state of the art of already well-established as well more recent methods. The hierarchical, partitioning and fuzzy approaches will be discussed amongst others. The authors review the difficulty of these classical methods in tackling the high dimensionality, sparsity and scalability. Chapter 2 discusses the interests of coclustering, presenting different approaches and defining a co-cluster. The authors focus on co-clustering as a simultaneous clustering and discuss the cases of binary, continuous and co-occurrence data. The criteria and algorithms are described and illustrated on simulated and real data. Chapter 3 considers co-clustering as a model-based co-clustering. A latent block model is defined for different kinds of data. The estimation of parameters and co-clustering is tackled under two approaches: maximum likelihood and classification maximum likelihood. Hard and soft algorithms are described and applied on simulated and real data. Chapter 4 considers co-clustering as a matrix approximation. The trifactorization approach is considered and algorithms based on update rules are described. Links with numerical and probabi

7317.23 рублей

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Christopher Price Microsoft Big Data Solutions

Tap the power of Big Data with Microsoft technologies Big Data is here, and Microsoft's new Big Data platform is a valuable tool to help your company get the very most out of it. This timely book shows you how to use HDInsight along with HortonWorks Data Platform for Windows to store, manage, analyze, and share Big Data throughout the enterprise. Focusing primarily on Microsoft and HortonWorks technologies but also covering open source tools, Microsoft Big Data Solutions explains best practices, covers on-premises and cloud-based solutions, and features valuable case studies. Best of all, it helps you integrate these new solutions with technologies you already know, such as SQL Server and Hadoop. Walks you through how to integrate Big Data solutions in your company using Microsoft's HDInsight Server, HortonWorks Data Platform for Windows, and open source tools Explores both on-premises and cloud-based solutions Shows how to store, manage, analyze, and share Big Data through the enterprise Covers topics such as Microsoft's approach to Big Data, installing and configuring HortonWorks Data Platform for Windows, integrating Big Data with SQL Server, visualizing data with Microsoft and HortonWorks BI tools, and more Helps you build and execute a Big Data plan Includes contributions from the Microsoft and HortonWorks Big Data product teams If you need a detailed roadmap for designing and implementing a fully deployed Big Data solution, you'll want Microsoft Big Data Solutions.

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Harvey Goldstein Methodological Developments in Data Linkage

A comprehensive compilation of new developments in data linkage methodology The increasing availability of large administrative databases has led to a dramatic rise in the use of data linkage, yet the standard texts on linkage are still those which describe the seminal work from the 1950-60s, with some updates. Linkage and analysis of data across sources remains problematic due to lack of discriminatory and accurate identifiers, missing data and regulatory issues. Recent developments in data linkage methodology have concentrated on bias and analysis of linked data, novel approaches to organising relationships between databases and privacy-preserving linkage. Methodological Developments in Data Linkage brings together a collection of contributions from members of the international data linkage community, covering cutting edge methodology in this field. It presents opportunities and challenges provided by linkage of large and often complex datasets, including analysis problems, legal and security aspects, models for data access and the development of novel research areas. New methods for handling uncertainty in analysis of linked data, solutions for anonymised linkage and alternative models for data collection are also discussed. Key Features: Presents cutting edge methods for a topic of increasing importance to a wide range of research areas, with applications to data linkage systems internationally Covers the essential issues associated with data linkage today Includes examples based on real data linkage systems, highlighting the opportunities, successes and challenges that the increasing availability of linkage data provides Novel approach incorporates technical aspects of both linkage, management and analysis of linked data This book will be of core interest to academics, government employees, data holders, data managers, analysts and statisticians who use administrative data. It will also appeal to researchers in a variety of areas, including epidemiology, biostatistics, social statistics, informatics, policy and public health.

6436.97 рублей

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Anders Wallgren Register-based Statistics. Statistical Methods for Administrative Data

This book provides a comprehensive and up to date treatment of theory and practical implementation in Register-based statistics. It begins by defining the area, before explaining how to structure such systems, as well as detailing alternative approaches. It explains how to create statistical registers, how to implement quality assurance, and the use of IT systems for register-based statistics. Further to this, clear details are given about the practicalities of implementing such statistical methods, such as protection of privacy and the coordination and coherence of such an undertaking. This edition offers a full understanding of both the principles and practices of this increasingly popular area of statistics, and can be considered a first step to a more systematic way of working with register-statistical issues. This book addresses the growing global interest in the topic and employs a much broader, more international approach than the 1st edition. New chapters explore different kinds of register-based surveys, such as preconditions for register-based statistics and comparing sample survey and administrative data. Furthermore, the authors present discussions on register-based census, national accounts and the transition towards a register-based system as well as presenting new chapters on quality assessment of administrative sources and production process quality.

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Chunlei Tang The Data Industry. The Business and Economics of Information and Big Data

Provides an introduction of the data industry to the field of economics This book bridges the gap between economics and data science to help data scientists understand the economics of big data, and enable economists to analyze the data industry. It begins by explaining data resources and introduces the data asset. This book defines a data industry chain, enumerates data enterprises’ business models versus operating models, and proposes a mode of industrial development for the data industry. The author describes five types of enterprise agglomerations, and multiple industrial cluster effects. A discussion on the establishment and development of data industry related laws and regulations is provided. In addition, this book discusses several scenarios on how to convert data driving forces into productivity that can then serve society. This book is designed to serve as a reference and training guide for ata scientists, data-oriented managers and executives, entrepreneurs, scholars, and government employees. Defines and develops the concept of a “Data Industry,” and explains the economics of data to data scientists and statisticians Includes numerous case studies and examples from a variety of industries and disciplines Serves as a useful guide for practitioners and entrepreneurs in the business of data technology The Data Industry: The Business and Economics of Information and Big Data is a resource for practitioners in the data science industry, government, and students in economics, business, and statistics. CHUNLEI TANG, Ph.D., is a research fellow at Harvard University. She is the co-founder of Fudan’s Institute for Data Industry and proposed the concept of the “data industry”. She received a Ph.D. in Computer and Software Theory in 2012 and a Master of Software Engineering in 2006 from Fudan University, Shanghai, China.

5794.64 рублей

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Vera Pawlowsky-Glahn Modeling and Analysis of Compositional Data

Modeling and Analysis of Compositional Data presents a practical and comprehensive introduction to the analysis of compositional data along with numerous examples to illustrate both theory and application of each method. Based upon short courses delivered by the authors, it provides a complete and current compendium of fundamental to advanced methodologies along with exercises at the end of each chapter to improve understanding, as well as data and a solutions manual which is available on an accompanying website. Complementing Pawlowsky-Glahn’s earlier collective text that provides an overview of the state-of-the-art in this field, Modeling and Analysis of Compositional Data fills a gap in the literature for a much-needed manual for teaching, self learning or consulting.

7901.1 рублей

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David J. A. Evans Till. A Glacial Process Sedimentology

Provides the first comprehensive review of the current state of the science on tills It is critical that glacial scientists continue to refine their interpretations of ancient archives of subglacial processes, specifically those represented by tills and associated deposits, as they form the most widespread and accessible record of processes at the ice-bed interface. Unfortunately, despite a long history of investigation and a lexicon of process-based nomenclature, glacial sedimentologists have yet to reach a consensus on diagnostic criteria for identifying till genesis in the geological record. What should be called till? Based on the author’s extensive field research, as well as the latest literature on the subject, this book attempts to provide a definitive answer to that question. It critically reviews the global till literature and experimental and laboratory-based assessments of subglacial processes, as well as the theoretical constructs that have emerged from process sedimentology over the past century. Drawing on a wide range of knowledge bases, David Evans develops a more precise, contemporary till nomenclature and new investigatory strategies for understanding a critical aspect of glacial process sedimentology. Provides an in-depth discussion of subglacial sedimentary processes, with an emphasis on the origins of till matrix and terminal grade and the latest observations on till evolution Describes contemporary laboratory and modelling experiments on till evolution and techniques for measuring strain signatures in glacial deposits Develops an updated till nomenclature based on an array of knowledge bases and describes new strategies for field description and analysis of glacial diamictons Written by an internationally recognised expert in the field, this book represents an important step forward in the modern understanding of glacial process sedimentology. As such, Till: A Glacial Process Sedimentology is an indispensable resource for advanced undergraduates and researchers in sedimentology, glacier science and related areas.

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Pushpak Sarkar Data as a Service. A Framework for Providing Reusable Enterprise Data Services

Data as a Service shows how organizations can leverage “data as a service” by providing real-life case studies on the various and innovative architectures and related patterns Comprehensive approach to introducing data as a service in any organization A reusable and flexible SOA based architecture framework Roadmap to introduce ‘big data as a service’ for potential clients Presents a thorough description of each component in the DaaS reference architecture so readers can implement solutions

5070.49 рублей

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Daniel Larose T. Discovering Knowledge in Data. An Introduction to Data Mining

The field of data mining lies at the confluence of predictive analytics, statistical analysis, and business intelligence. Due to the ever-increasing complexity and size of data sets and the wide range of applications in computer science, business, and health care, the process of discovering knowledge in data is more relevant than ever before. This book provides the tools needed to thrive in today’s big data world. The author demonstrates how to leverage a company’s existing databases to increase profits and market share, and carefully explains the most current data science methods and techniques. The reader will “learn data mining by doing data mining”. By adding chapters on data modelling preparation, imputation of missing data, and multivariate statistical analysis, Discovering Knowledge in Data, Second Edition remains the eminent reference on data mining. The second edition of a highly praised, successful reference on data mining, with thorough coverage of big data applications, predictive analytics, and statistical analysis. Includes new chapters on Multivariate Statistics, Preparing to Model the Data, and Imputation of Missing Data, and an Appendix on Data Summarization and Visualization Offers extensive coverage of the R statistical programming language Contains 280 end-of-chapter exercises Includes a companion website for university instructors who adopt the book

7077.61 рублей

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Herbert W. Seliger Second Language Research Methods

Based on a set of four research parameters, this book discusses the development of research questions and hypotheses, naturalistic and experimental research, data collection, and validation of research instruments. Each chapter includes examples and activities.

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Jelke Bethlehem Online Panel Research. A Data Quality Perspective

Provides new insights into the accuracy and value of online panels for completing surveys Over the last decade, there has been a major global shift in survey and market research towards data collection, using samples selected from online panels. Yet despite their widespread use, remarkably little is known about the quality of the resulting data. This edited volume is one of the first attempts to carefully examine the quality of the survey data being generated by online samples. It describes some of the best empirically-based research on what has become a very important yet controversial method of collecting data. Online Panel Research presents 19 chapters of previously unpublished work addressing a wide range of topics, including coverage bias, nonresponse, measurement error, adjustment techniques, the relationship between nonresponse and measurement error, impact of smartphone adoption on data collection, Internet rating panels, and operational issues. The datasets used to prepare the analyses reported in the chapters are available on the accompanying website: www.wiley.com/go/online_panel Covers controversial topics such as professional respondents, speeders, and respondent validation. Addresses cutting-edge topics such as the challenge of smartphone survey completion, software to manage online panels, and Internet and mobile ratings panels. Discusses and provides examples of comparison studies between online panels and other surveys or benchmarks. Describes adjustment techniques to improve sample representativeness. Addresses coverage, nonresponse, attrition, and the relationship between nonresponse and measurement error with examples using data from the United States and Europe. Addresses practical questions such as motivations for joining an online panel and best practices for managing communications with panelists. Presents a meta-analysis of determinants of response quantity. Features contributions from 50 international authors with a wide variety of backgrounds and expertise. This book will be an invaluable resource for opinion and market researchers, academic researchers relying on web-based data collection, governmental researchers, statisticians, psychologists, sociologists, and other research practitioners.

6741.62 рублей

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Jonathan Ajo-Franklin Geological Carbon Storage. Subsurface Seals and Caprock Integrity

Geological Carbon Storage Subsurface Seals and Caprock Integrity Seals and caprocks are an essential component of subsurface hydrogeological systems, guiding the movement and entrapment of hydrocarbon and other fluids. Geological Carbon Storage: Subsurface Seals and Caprock Integrity offers a survey of the wealth of recent scientific work on caprock integrity with a focus on the geological controls of permanent and safe carbon dioxide storage, and the commercial deployment of geological carbon storage. Volume highlights include: Low-permeability rock characterization from the pore scale to the core scale Flow and transport properties of low-permeability rocks Fundamentals of fracture generation, self-healing, and permeability Coupled geochemical, transport and geomechanical processes in caprock Analysis of caprock behavior from natural analogues Geochemical and geophysical monitoring techniques of caprock failure and integrity Potential environmental impacts of carbon dioxide migration on groundwater resources Carbon dioxide leakage mitigation and remediation techniques Geological Carbon Storage: Subsurface Seals and Caprock Integrity is an invaluable resource for geoscientists from academic and research institutions with interests in energy and environment-related problems, as well as professionals in the field.

18270.2 рублей

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Johnson Wayne P. Making Sense of Data I. A Practical Guide to Exploratory Data Analysis and Data Mining

Praise for the First Edition “…a well-written book on data analysis and data mining that provides an excellent foundation…” —CHOICE “This is a must-read book for learning practical statistics and data analysis…” —Computing Reviews.com A proven go-to guide for data analysis, Making Sense of Data I: A Practical Guide to Exploratory Data Analysis and Data Mining, Second Edition focuses on basic data analysis approaches that are necessary to make timely and accurate decisions in a diverse range of projects. Based on the authors’ practical experience in implementing data analysis and data mining, the new edition provides clear explanations that guide readers from almost every field of study. In order to facilitate the needed steps when handling a data analysis or data mining project, a step-by-step approach aids professionals in carefully analyzing data and implementing results, leading to the development of smarter business decisions. The tools to summarize and interpret data in order to master data analysis are integrated throughout, and the Second Edition also features: Updated exercises for both manual and computer-aided implementation with accompanying worked examples New appendices with coverage on the freely available Traceis™ software, including tutorials using data from a variety of disciplines such as the social sciences, engineering, and finance New topical coverage on multiple linear regression and logistic regression to provide a range of widely used and transparent approaches Additional real-world examples of data preparation to establish a practical background for making decisions from data Making Sense of Data I: A Practical Guide to Exploratory Data Analysis and Data Mining, Second Edition is an excellent reference for researchers and professionals who need to achieve effective decision making from data. The Second Edition is also an ideal textbook for undergraduate and graduate-level courses in data analysis and data mining and is appropriate for cross-disciplinary courses found within computer science and engineering departments.

5939.26 рублей

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Jennifer Madans Question Evaluation Methods. Contributing to the Science of Data Quality

Insightful observations on common question evaluation methods and best practices for data collection in survey research Featuring contributions from leading researchers and academicians in the field of survey research, Question Evaluation Methods: Contributing to the Science of Data Quality sheds light on question response error and introduces an interdisciplinary, cross-method approach that is essential for advancing knowledge about data quality and ensuring the credibility of conclusions drawn from surveys and censuses. Offering a variety of expert analyses of question evaluation methods, the book provides recommendations and best practices for researchers working with data in the health and social sciences. Based on a workshop held at the National Center for Health Statistics (NCHS), this book presents and compares various question evaluation methods that are used in modern-day data collection and analysis. Each section includes an introduction to a method by a leading authority in the field, followed by responses from other experts that outline related strengths, weaknesses, and underlying assumptions. Topics covered include: Behavior coding Cognitive interviewing Item response theory Latent class analysis Split-sample experiments Multitrait-multimethod experiments Field-based data methods A concluding discussion identifies common themes across the presented material and their relevance to the future of survey methods, data analysis, and the production of Federal statistics. Together, the methods presented in this book offer researchers various scientific approaches to evaluating survey quality to ensure that the responses to these questions result in reliable, high-quality data. Question Evaluation Methods is a valuable supplement for courses on questionnaire design, survey methods, and evaluation methods at the upper-undergraduate and graduate levels. it also serves as a reference for government statisticians, survey methodologists, and researchers and practitioners who carry out survey research in the areas of the social and health sciences.

6741.62 рублей

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Xu Rui Clustering

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.

11954.66 рублей

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I. Gusti Ngurah Agung Cross Section and Experimental Data Analysis Using EViews

A practical guide to selecting and applying the most appropriate model for analysis of cross section data using EViews. «This book is a reflection of the vast experience and knowledge of the author. It is a useful reference for students and practitioners dealing with cross sectional data analysis … The strength of the book lies in its wealth of material and well structured guidelines …» Prof. Yohanes Eko Riyanto, Nanyang Technological University, Singapore «This is superb and brilliant. Prof. Agung has skilfully transformed his best experiences into new knowledge … creating a new way of understanding data analysis.» Dr. I Putu Gede Ary Suta, The Ary Suta Center, Jakarta Basic theoretical concepts of statistics as well as sampling methods are often misinterpreted by students and less experienced researchers. This book addresses this issue by providing a hands-on practical guide to conducting data analysis using EViews combined with a variety of illustrative models (and their extensions). Models having numerically dependent variables based on a cross-section data set (such as univariate, multivariate and nonlinear models as well as non-parametric regressions) are concentrated on. It is shown that a wide variety of hypotheses can easily be tested using EViews. Cross Section and Experimental Data Analysis Using EViews: Provides step-by-step directions on how to apply EViews to cross section data analysis – from multivariate analysis and nonlinear models to non-parametric regression Presents a method to test for all possible hypotheses based on each model Proposes a new method for data analysis based on a multifactorial design model Demonstrates that statistical summaries in the form of tabulations are invaluable inputs for strategic decision making Contains 200 examples with special notes and comments based on the author’s own empirical findings as well as over 400 illustrative outputs of regressions from EViews Techniques are illustrated through practical examples from real situations Comes with supplementary material, including work-files containing selected equation and system specifications that have been applied in the book This user-friendly introduction to EViews is ideal for Advanced undergraduate and graduate students taking finance, econometrics, population, or public policy courses, as well as applied policy researchers.

11040.93 рублей

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I. Gusti Ngurah Agung Panel Data Analysis using EViews

A comprehensive and accessible guide to panel data analysis using EViews software This book explores the use of EViews software in creating panel data analysis using appropriate empirical models and real datasets. Guidance is given on developing alternative descriptive statistical summaries for evaluation and providing policy analysis based on pool panel data. Various alternative models based on panel data are explored, including univariate general linear models, fixed effect models and causal models, and guidance on the advantages and disadvantages of each one is given. Panel Data Analysis using EViews: Provides step-by-step guidance on how to apply EViews software to panel data analysis using appropriate empirical models and real datasets. Examines a variety of panel data models along with the author’s own empirical findings, demonstrating the advantages and limitations of each model. Presents growth models, time-related effects models, and polynomial models, in addition to the models which are commonly applied for panel data. Includes more than 250 examples divided into three groups of models (stacked, unstacked, and structured panel data), together with notes and comments. Provides guidance on which models not to use in a given scenario, along with advice on viable alternatives. Explores recent new developments in panel data analysis An essential tool for advanced undergraduate or graduate students and applied researchers in finance, econometrics and population studies. Statisticians and data analysts involved with data collected over long time periods will also find this book a useful resource.

11040.93 рублей

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Cluster or co-cluster analyses are important tools in a variety of scientific areas. The introduction of this book presents a state of the art of already well-established, as well as more recent methods of co-clustering. The authors mainly deal with the two-mode partitioning under different approaches, but pay particular attention to a probabilistic approach. Chapter 1 concerns clustering in general and the model-based clustering in particular. The authors briefly review the classical clustering methods and focus on the mixture model. They present and discuss the use of different mixtures adapted to different types of data. The algorithms used are described and related works with different classical methods are presented and commented upon. This chapter is useful in tackling the problem of co-clustering under the mixture approach. Chapter 2 is devoted to the latent block model proposed in the mixture approach context. The authors discuss this model in detail and present its interest regarding co-clustering. Various algorithms are presented in a general context. Chapter 3 focuses on binary and categorical data. It presents, in detail, the appropriated latent block mixture models. Variants of these models and algorithms are presented and illustrated using examples. Chapter 4 focuses on contingency data. Mutual information, phi-squared and model-based co-clustering are studied. Models, algorithms and connections among different approaches are described and illustrated. Chapter 5 presents the case of continuous data. In the same way, the different approaches used in the previous chapters are extended to this situation. Contents 1. Cluster Analysis. 2. Model-Based Co-Clustering. 3. Co-Clustering of Binary and Categorical Data. 4. Co-Clustering of Contingency Tables. 5. Co-Clustering of Continuous Data. About the Authors Gérard Govaert is Professor at the University of Technology of Compiègne, France. He is also a member of the CNRS Laboratory Heudiasyc (Heuristic and diagnostic of complex systems). His research interests include latent structure modeling, model selection, model-based cluster analysis, block clustering and statistical pattern recognition. He is one of the authors of the MIXMOD (MIXtureMODelling) software. Mohamed Nadif is Professor at the University of Paris-Descartes, France, where he is a member of LIPADE (Paris Descartes computer science laboratory) in the Mathematics and Computer Science department. His research interests include machine learning, data mining, model-based cluster analysis, co-clustering, factorization and data analysis. Cluster Analysis is an important tool in a variety of scientific areas. Chapter 1 briefly presents a state of the art of already well-established as well more recent methods. The hierarchical, partitioning and fuzzy approaches will be discussed amongst others. The authors review the difficulty of these classical methods in tackling the high dimensionality, sparsity and scalability. Chapter 2 discusses the interests of coclustering, presenting different approaches and defining a co-cluster. The authors focus on co-clustering as a simultaneous clustering and discuss the cases of binary, continuous and co-occurrence data. The criteria and algorithms are described and illustrated on simulated and real data. Chapter 3 considers co-clustering as a model-based co-clustering. A latent block model is defined for different kinds of data. The estimation of parameters and co-clustering is tackled under two approaches: maximum likelihood and classification maximum likelihood. Hard and soft algorithms are described and applied on simulated and real data. Chapter 4 considers co-clustering as a matrix approximation. The trifactorization approach is considered and algorithms based on update rules are described. Links with numerical and probabi

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