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Title Applied Biclustering Methods for Big and High Dimensional Data Using R
Author Adetayo Kasim
Publisher CRC Press
Release Date 2016-08-18
Category Mathematics
Total Pages 433
ISBN 9781315356396
Language English, Spanish, and French
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Book Summary:

Proven Methods for Big Data Analysis As big data has become standard in many application areas, challenges have arisen related to methodology and software development, including how to discover meaningful patterns in the vast amounts of data. Addressing these problems, Applied Biclustering Methods for Big and High-Dimensional Data Using R shows how to apply biclustering methods to find local patterns in a big data matrix. The book presents an overview of data analysis using biclustering methods from a practical point of view. Real case studies in drug discovery, genetics, marketing research, biology, toxicity, and sports illustrate the use of several biclustering methods. References to technical details of the methods are provided for readers who wish to investigate the full theoretical background. All the methods are accompanied with R examples that show how to conduct the analyses. The examples, software, and other materials are available on a supplementary website.

Title Biopharmaceutical Applied Statistics Symposium
Author Karl E. Peace
Publisher Springer
Release Date 2018-09-03
Category Medical
Total Pages 426
ISBN 9789811078200
Language English, Spanish, and French
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Book Summary:

This BASS book Series publishes selected high-quality papers reflecting recent advances in the design and biostatistical analysis of biopharmaceutical experiments – particularly biopharmaceutical clinical trials. The papers were selected from invited presentations at the Biopharmaceutical Applied Statistics Symposium (BASS), which was founded by the first Editor in 1994 and has since become the premier international conference in biopharmaceutical statistics. The primary aims of the BASS are: 1) to raise funding to support graduate students in biostatistics programs, and 2) to provide an opportunity for professionals engaged in pharmaceutical drug research and development to share insights into solving the problems they encounter. The BASS book series is initially divided into three volumes addressing: 1) Design of Clinical Trials; 2) Biostatistical Analysis of Clinical Trials; and 3) Pharmaceutical Applications. This book is the third of the 3-volume book series. The topics covered include: Targeted Learning of Optimal Individualized Treatment Rules under Cost Constraints, Uses of Mixture Normal Distribution in Genomics and Otherwise, Personalized Medicine – Design Considerations, Adaptive Biomarker Subpopulation and Tumor Type Selection in Phase III Oncology Trials, High Dimensional Data in Genomics; Synergy or Additivity - The Importance of Defining the Primary Endpoint, Full Bayesian Adaptive Dose Finding Using Toxicity Probability Interval (TPI), Alpha-recycling for the Analyses of Primary and Secondary Endpoints of Clinical Trials, Expanded Interpretations of Results of Carcinogenicity Studies of Pharmaceuticals, Randomized Clinical Trials for Orphan Drug Development, Mediation Modeling in Randomized Trials with Non-normal Outcome Variables, Statistical Considerations in Using Images in Clinical Trials, Interesting Applications over 30 Years of Consulting, Uncovering Fraud, Misconduct and Other Data Quality Issues in Clinical Trials, Development and Evaluation of High Dimensional Prognostic Models, and Design and Analysis of Biosimilar Studies.

Title Clinical Trial Optimization Using R
Author Alex Dmitrienko
Publisher CRC Press
Release Date 2017-08-10
Category Mathematics
Total Pages 319
ISBN 9781498735087
Language English, Spanish, and French
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Book Summary:

Clinical Trial Optimization Using R explores a unified and broadly applicable framework for optimizing decision making and strategy selection in clinical development, through a series of examples and case studies. It provides the clinical researcher with a powerful evaluation paradigm, as well as supportive R tools, to evaluate and select among simultaneous competing designs or analysis options. It is applicable broadly to statisticians and other quantitative clinical trialists, who have an interest in optimizing clinical trials, clinical trial programs, or associated analytics and decision making. This book presents in depth the Clinical Scenario Evaluation (CSE) framework, and discusses optimization strategies, including the quantitative assessment of tradeoffs. A variety of common development challenges are evaluated as case studies, and used to show how this framework both simplifies and optimizes strategy selection. Specific settings include optimizing adaptive designs, multiplicity and subgroup analysis strategies, and overall development decision-making criteria around Go/No-Go. After this book, the reader will be equipped to extend the CSE framework to their particular development challenges as well.

Title Market Segmentation Analysis
Author Sara Dolnicar
Publisher Springer
Release Date 2018-07-20
Category Business & Economics
Total Pages 324
ISBN 9789811088186
Language English, Spanish, and French
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Book Summary:

This book is published open access under a CC BY 4.0 license. This open access book offers something for everyone working with market segmentation: practical guidance for users of market segmentation solutions; organisational guidance on implementation issues; guidance for market researchers in charge of collecting suitable data; and guidance for data analysts with respect to the technical and statistical aspects of market segmentation analysis. Even market segmentation experts will find something new, including an approach to exploring data structure and choosing a suitable number of market segments, and a vast array of useful visualisation techniques that make interpretation of market segments and selection of target segments easier. The book talks the reader through every single step, every single potential pitfall, and every single decision that needs to be made to ensure market segmentation analysis is conducted as well as possible. All calculations are accompanied not only with a detailed explanation, but also with R code that allows readers to replicate any aspect of what is being covered in the book using R, the open-source environment for statistical computing and graphics.

Title Statistical Learning with Sparsity
Author Trevor Hastie
Publisher CRC Press
Release Date 2015-05-07
Category Business & Economics
Total Pages 367
ISBN 9781498712170
Language English, Spanish, and French
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Book Summary:

Discover New Methods for Dealing with High-Dimensional Data A sparse statistical model has only a small number of nonzero parameters or weights; therefore, it is much easier to estimate and interpret than a dense model. Statistical Learning with Sparsity: The Lasso and Generalizations presents methods that exploit sparsity to help recover the underlying signal in a set of data. Top experts in this rapidly evolving field, the authors describe the lasso for linear regression and a simple coordinate descent algorithm for its computation. They discuss the application of l1 penalties to generalized linear models and support vector machines, cover generalized penalties such as the elastic net and group lasso, and review numerical methods for optimization. They also present statistical inference methods for fitted (lasso) models, including the bootstrap, Bayesian methods, and recently developed approaches. In addition, the book examines matrix decomposition, sparse multivariate analysis, graphical models, and compressed sensing. It concludes with a survey of theoretical results for the lasso. In this age of big data, the number of features measured on a person or object can be large and might be larger than the number of observations. This book shows how the sparsity assumption allows us to tackle these problems and extract useful and reproducible patterns from big datasets. Data analysts, computer scientists, and theorists will appreciate this thorough and up-to-date treatment of sparse statistical modeling.

Co Clustering by Gérard Govaert

Title Co Clustering
Author Gérard Govaert
Publisher John Wiley & Sons
Release Date 2013-12-11
Category Computers
Total Pages 256
ISBN 9781118649503
Language English, Spanish, and French
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Book Summary:

Cluster or co-cluster analyses are important tools in a variety ofscientific areas. The introduction of this book presents a state ofthe art of already well-established, as well as more recent methodsof co-clustering. The authors mainly deal with the two-modepartitioning under different approaches, but pay particularattention to a probabilistic approach. Chapter 1 concerns clustering in general and the model-basedclustering in particular. The authors briefly review the classicalclustering methods and focus on the mixture model. They present anddiscuss the use of different mixtures adapted to different types ofdata. The algorithms used are described and related works withdifferent classical methods are presented and commented upon. Thischapter is useful in tackling the problem of co-clustering under the mixture approach. Chapter 2 is devoted tothe latent block model proposed in the mixture approach context.The authors discuss this model in detail and present its interestregarding co-clustering. Various algorithms are presented in ageneral context. Chapter 3 focuses on binary and categorical data.It presents, in detail, the appropriated latent block mixturemodels. Variants of these models and algorithms are presented andillustrated using examples. Chapter 4 focuses on contingency data.Mutual information, phi-squared and model-based co-clustering arestudied. Models, algorithms and connections among differentapproaches are described and illustrated. Chapter 5 presents thecase of continuous data. In the same way, the different approachesused 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 Technologyof Compiègne, France. He is also a member of the CNRSLaboratory Heudiasyc (Heuristic and diagnostic of complex systems).His research interests include latent structure modeling, modelselection, model-based cluster analysis, block clustering andstatistical pattern recognition. He is one of the authors of theMIXMOD (MIXtureMODelling) software. Mohamed Nadif is Professor at the University of Paris-Descartes,France, where he is a member of LIPADE (Paris Descartes computerscience laboratory) in the Mathematics and Computer Sciencedepartment. His research interests include machine learning, datamining, model-based cluster analysis, co-clustering, factorizationand data analysis. Cluster Analysis is an important tool in a variety of scientificareas. Chapter 1 briefly presents a state of the art of alreadywell-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 intackling the high dimensionality, sparsity and scalability. Chapter2 discusses the interests of coclustering, presenting differentapproaches and defining a co-cluster. The authors focus onco-clustering as a simultaneous clustering and discuss the cases ofbinary, continuous and co-occurrence data. The criteria andalgorithms are described and illustrated on simulated and realdata. Chapter 3 considers co-clustering as a model-basedco-clustering. A latent block model is defined for different kindsof data. The estimation of parameters and co-clustering is tackledunder two approaches: maximum likelihood and classification maximumlikelihood. Hard and soft algorithms are described and applied onsimulated and real data. Chapter 4 considers co-clustering as amatrix approximation. The trifactorization approach is consideredand algorithms based on update rules are described. Links withnumerical and probabilistic approaches are established. Acombination of algorithms are proposed and evaluated on simulatedand real data. Chapter 5 considers a co-clustering or bi-clusteringas the search for coherent co-clusters in biological terms or theextraction of co-clusters under conditions. Classical algorithmswill be described and evaluated on simulated and real data.Different indices to evaluate the quality of coclusters are notedand used in numerical experiments.

Title Plaid Models for Gene Expression Data
Author Laura Lazzeroni
Publisher Unknown
Release Date 2000
Category
Total Pages 25
ISBN OCLC:81031930
Language English, Spanish, and French
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Book Summary:

Data Clustering by Charu C. Aggarwal

Title Data Clustering
Author Charu C. Aggarwal
Publisher CRC Press
Release Date 2018-09-03
Category Business & Economics
Total Pages 652
ISBN 9781315360416
Language English, Spanish, and French
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Book Summary:

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.

Title Data Mining Concepts and Techniques
Author Jiawei Han
Publisher Elsevier
Release Date 2011-06-09
Category Computers
Total Pages 744
ISBN 0123814804
Language English, Spanish, and French
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Book Summary:

Data Mining: Concepts and Techniques provides the concepts and techniques in processing gathered data or information, which will be used in various applications. Specifically, it explains data mining and the tools used in discovering knowledge from the collected data. This book is referred as the knowledge discovery from data (KDD). It focuses on the feasibility, usefulness, effectiveness, and scalability of techniques of large data sets. After describing data mining, this edition explains the methods of knowing, preprocessing, processing, and warehousing data. It then presents information about data warehouses, online analytical processing (OLAP), and data cube technology. Then, the methods involved in mining frequent patterns, associations, and correlations for large data sets are described. The book details the methods for data classification and introduces the concepts and methods for data clustering. The remaining chapters discuss the outlier detection and the trends, applications, and research frontiers in data mining. This book is intended for Computer Science students, application developers, business professionals, and researchers who seek information on data mining. Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of your data

Title Grouping Multidimensional Data
Author Jacob Kogan
Publisher Taylor & Francis
Release Date 2006-02-10
Category Computers
Total Pages 268
ISBN 354028348X
Language English, Spanish, and French
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Book Summary:

Publisher description

Title Neural Approaches to Dynamics of Signal Exchanges
Author Anna Esposito
Publisher Springer Nature
Release Date 2019-09-18
Category Technology & Engineering
Total Pages 525
ISBN 9789811389504
Language English, Spanish, and French
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Book Summary:

The book presents research that contributes to the development of intelligent dialog systems to simplify diverse aspects of everyday life, such as medical diagnosis and entertainment. Covering major thematic areas: machine learning and artificial neural networks; algorithms and models; and social and biometric data for applications in human–computer interfaces, it discusses processing of audio-visual signals for the detection of user-perceived states, the latest scientific discoveries in processing verbal (lexicon, syntax, and pragmatics), auditory (voice, intonation, vocal expressions) and visual signals (gestures, body language, facial expressions), as well as algorithms for detecting communication disorders, remote health-status monitoring, sentiment and affect analysis, social behaviors and engagement. Further, it examines neural and machine learning algorithms for the implementation of advanced telecommunication systems, communication with people with special needs, emotion modulation by computer contents, advanced sensors for tracking changes in real-life and automatic systems, as well as the development of advanced human–computer interfaces. The book does not focus on solving a particular problem, but instead describes the results of research that has positive effects in different fields and applications.

Title Big Data Analytics Systems Algorithms Applications
Author C.S.R. Prabhu
Publisher Springer Nature
Release Date 2019-10-14
Category Computers
Total Pages 412
ISBN 9789811500947
Language English, Spanish, and French
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Book Summary:

This book provides a comprehensive survey of techniques, technologies and applications of Big Data and its analysis. The Big Data phenomenon is increasingly impacting all sectors of business and industry, producing an emerging new information ecosystem. On the applications front, the book offers detailed descriptions of various application areas for Big Data Analytics in the important domains of Social Semantic Web Mining, Banking and Financial Services, Capital Markets, Insurance, Advertisement, Recommendation Systems, Bio-Informatics, the IoT and Fog Computing, before delving into issues of security and privacy. With regard to machine learning techniques, the book presents all the standard algorithms for learning – including supervised, semi-supervised and unsupervised techniques such as clustering and reinforcement learning techniques to perform collective Deep Learning. Multi-layered and nonlinear learning for Big Data are also covered. In turn, the book highlights real-life case studies on successful implementations of Big Data Analytics at large IT companies such as Google, Facebook, LinkedIn and Microsoft. Multi-sectorial case studies on domain-based companies such as Deutsche Bank, the power provider Opower, Delta Airlines and a Chinese City Transportation application represent a valuable addition. Given its comprehensive coverage of Big Data Analytics, the book offers a unique resource for undergraduate and graduate students, researchers, educators and IT professionals alike.

Title Understanding High Dimensional Spaces
Author David B. Skillicorn
Publisher Springer Science & Business Media
Release Date 2012-09-24
Category Computers
Total Pages 108
ISBN 9783642333989
Language English, Spanish, and French
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Book Summary:

High-dimensional spaces arise as a way of modelling datasets with many attributes. Such a dataset can be directly represented in a space spanned by its attributes, with each record represented as a point in the space with its position depending on its attribute values. Such spaces are not easy to work with because of their high dimensionality: our intuition about space is not reliable, and measures such as distance do not provide as clear information as we might expect. There are three main areas where complex high dimensionality and large datasets arise naturally: data collected by online retailers, preference sites, and social media sites, and customer relationship databases, where there are large but sparse records available for each individual; data derived from text and speech, where the attributes are words and so the corresponding datasets are wide, and sparse; and data collected for security, defense, law enforcement, and intelligence purposes, where the datasets are large and wide. Such datasets are usually understood either by finding the set of clusters they contain or by looking for the outliers, but these strategies conceal subtleties that are often ignored. In this book the author suggests new ways of thinking about high-dimensional spaces using two models: a skeleton that relates the clusters to one another; and boundaries in the empty space between clusters that provide new perspectives on outliers and on outlying regions. The book will be of value to practitioners, graduate students and researchers.

Title Programming Collective Intelligence
Author Toby Segaran
Publisher "O'Reilly Media, Inc."
Release Date 2007-08-16
Category Computers
Total Pages 362
ISBN 9780596550684
Language English, Spanish, and French
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Book Summary:

Want to tap the power behind search rankings, product recommendations, social bookmarking, and online matchmaking? This fascinating book demonstrates how you can build Web 2.0 applications to mine the enormous amount of data created by people on the Internet. With the sophisticated algorithms in this book, you can write smart programs to access interesting datasets from other web sites, collect data from users of your own applications, and analyze and understand the data once you've found it. Programming Collective Intelligence takes you into the world of machine learning and statistics, and explains how to draw conclusions about user experience, marketing, personal tastes, and human behavior in general -- all from information that you and others collect every day. Each algorithm is described clearly and concisely with code that can immediately be used on your web site, blog, Wiki, or specialized application. This book explains: Collaborative filtering techniques that enable online retailers to recommend products or media Methods of clustering to detect groups of similar items in a large dataset Search engine features -- crawlers, indexers, query engines, and the PageRank algorithm Optimization algorithms that search millions of possible solutions to a problem and choose the best one Bayesian filtering, used in spam filters for classifying documents based on word types and other features Using decision trees not only to make predictions, but to model the way decisions are made Predicting numerical values rather than classifications to build price models Support vector machines to match people in online dating sites Non-negative matrix factorization to find the independent features in a dataset Evolving intelligence for problem solving -- how a computer develops its skill by improving its own code the more it plays a game Each chapter includes exercises for extending the algorithms to make them more powerful. Go beyond simple database-backed applications and put the wealth of Internet data to work for you. "Bravo! I cannot think of a better way for a developer to first learn these algorithms and methods, nor can I think of a better way for me (an old AI dog) to reinvigorate my knowledge of the details." -- Dan Russell, Google "Toby's book does a great job of breaking down the complex subject matter of machine-learning algorithms into practical, easy-to-understand examples that can be directly applied to analysis of social interaction across the Web today. If I had this book two years ago, it would have saved precious time going down some fruitless paths." -- Tim Wolters, CTO, Collective Intellect

Title Data Mining with Rattle and R
Author Graham Williams
Publisher Springer Science & Business Media
Release Date 2011-08-04
Category Mathematics
Total Pages 374
ISBN 9781441998903
Language English, Spanish, and French
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Book Summary:

Data mining is the art and science of intelligent data analysis. By building knowledge from information, data mining adds considerable value to the ever increasing stores of electronic data that abound today. In performing data mining many decisions need to be made regarding the choice of methodology, the choice of data, the choice of tools, and the choice of algorithms. Throughout this book the reader is introduced to the basic concepts and some of the more popular algorithms of data mining. With a focus on the hands-on end-to-end process for data mining, Williams guides the reader through various capabilities of the easy to use, free, and open source Rattle Data Mining Software built on the sophisticated R Statistical Software. The focus on doing data mining rather than just reading about data mining is refreshing. The book covers data understanding, data preparation, data refinement, model building, model evaluation, and practical deployment. The reader will learn to rapidly deliver a data mining project using software easily installed for free from the Internet. Coupling Rattle with R delivers a very sophisticated data mining environment with all the power, and more, of the many commercial offerings.

Title Artificial Intelligence and Soft Computing ICAISC 2008
Author Leszek Rutkowski
Publisher Springer
Release Date 2008-06-19
Category Computers
Total Pages 1269
ISBN 9783540697312
Language English, Spanish, and French
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Book Summary:

This book constitutes the refereed proceedings of the 9th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2008, held in Zakopane, Poland, in June 2008. The 116 revised contributed papers presented were carefully reviewed and selected from 320 submissions. The papers are organized in topical sections on neural networks and their applications, fuzzy systems and their applications, evolutionary algorithms and their applications, classification, rule discovery and clustering, image analysis, speech and robotics, bioinformatics and medical applications, various problems of artificial intelligence, and agent systems.

Personalized Psychiatry by Bernhard Baune

Title Personalized Psychiatry
Author Bernhard Baune
Publisher Academic Press
Release Date 2019-10-16
Category Medical
Total Pages 604
ISBN 9780128131770
Language English, Spanish, and French
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Book Summary:

Personalized Psychiatry presents the first book to explore this novel field of biological psychiatry that covers both basic science research and its translational applications. The book conceptualizes personalized psychiatry and provides state-of-the-art knowledge on biological and neuroscience methodologies, all while integrating clinical phenomenology relevant to personalized psychiatry and discussing important principles and potential models. It is essential reading for advanced students and neuroscience and psychiatry researchers who are investigating the prevention and treatment of mental disorders. Combines neurobiology with basic science methodologies in genomics, epigenomics and transcriptomics Demonstrates how the statistical modeling of interacting biological and clinical information could transform the future of psychiatry Addresses fundamental questions and requirements for personalized psychiatry from a basic research and translational perspective

Title Modern Multivariate Statistical Techniques
Author Alan J. Izenman
Publisher Springer Science & Business Media
Release Date 2009-03-02
Category Mathematics
Total Pages 733
ISBN 0387781897
Language English, Spanish, and French
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Book Summary:

This is the first book on multivariate analysis to look at large data sets which describes the state of the art in analyzing such data. Material such as database management systems is included that has never appeared in statistics books before.

Understanding Complex Datasets by David Skillicorn

Title Understanding Complex Datasets
Author David Skillicorn
Publisher CRC Press
Release Date 2007-05-17
Category Computers
Total Pages 260
ISBN 1584888334
Language English, Spanish, and French
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Book Summary:

Making obscure knowledge about matrix decompositions widely available, Understanding Complex Datasets: Data Mining with Matrix Decompositions discusses the most common matrix decompositions and shows how they can be used to analyze large datasets in a broad range of application areas. Without having to understand every mathematical detail, the book helps you determine which matrix is appropriate for your dataset and what the results mean. Explaining the effectiveness of matrices as data analysis tools, the book illustrates the ability of matrix decompositions to provide more powerful analyses and to produce cleaner data than more mainstream techniques. The author explores the deep connections between matrix decompositions and structures within graphs, relating the PageRank algorithm of Google's search engine to singular value decomposition. He also covers dimensionality reduction, collaborative filtering, clustering, and spectral analysis. With numerous figures and examples, the book shows how matrix decompositions can be used to find documents on the Internet, look for deeply buried mineral deposits without drilling, explore the structure of proteins, detect suspicious emails or cell phone calls, and more. Concentrating on data mining mechanics and applications, this resource helps you model large, complex datasets and investigate connections between standard data mining techniques and matrix decompositions.

Title Data Science and Social Research
Author N. Carlo Lauro
Publisher Springer
Release Date 2017-11-17
Category Social Science
Total Pages 300
ISBN 9783319554778
Language English, Spanish, and French
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Book Summary:

This edited volume lays the groundwork for Social Data Science, addressing epistemological issues, methods, technologies, software and applications of data science in the social sciences. It presents data science techniques for the collection, analysis and use of both online and offline new (big) data in social research and related applications. Among others, the individual contributions cover topics like social media, learning analytics, clustering, statistical literacy, recurrence analysis and network analysis. Data science is a multidisciplinary approach based mainly on the methods of statistics and computer science, and its aim is to develop appropriate methodologies for forecasting and decision-making in response to an increasingly complex reality often characterized by large amounts of data (big data) of various types (numeric, ordinal and nominal variables, symbolic data, texts, images, data streams, multi-way data, social networks etc.) and from diverse sources. This book presents selected papers from the international conference on Data Science & Social Research, held in Naples, Italy in February 2016, and will appeal to researchers in the social sciences working in academia as well as in statistical institutes and offices.