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Data Mining: Concepts And Techniques (Morgan Kaufmann Series In Data Management System)
by Jiawei Han Jian Pei Micheline KamberData Mining: Concepts and Techniques (The Morgan Kaufmann Series in Data Management Systems) 3rd Edition
Data Mining: Concepts and Techniques (The Morgan Kaufmann Series in Data Management Systems)
by Jiawei Han Jian Pei Hanghang TongData Mining: Concepts and Techniques, Fourth Edition introduces concepts, principles, and methods for mining patterns, knowledge, and models from various kinds of data for diverse applications. Specifically, it delves into the processes for uncovering patterns and knowledge from massive collections of data, known as knowledge discovery from data, or KDD. It focuses on the feasibility, usefulness, effectiveness, and scalability of data mining techniques for large data sets. <p><p>After an introduction to the concept of data mining, the authors explain the methods for preprocessing, characterizing, and warehousing data. They then partition the data mining methods into several major tasks, introducing concepts and methods for mining frequent patterns, associations, and correlations for large data sets; data classification and model construction; cluster analysis; and outlier detection. Concepts and methods for deep learning are systematically introduced as one chapter. Finally, the book covers the trends, applications, and research frontiers in data mining. <p><p>Presents a comprehensive new chapter on deep learning, including improving training of deep learning models, convolutional neural networks, recurrent neural networks, and graph neural networks. Addresses advanced topics in one dedicated chapter: data mining trends and research frontiers, including mining rich data types (text, spatiotemporal data, and graph/networks), data mining applications (such as sentiment analysis, truth discovery, and information propagation), data mining methodologies and systems, and data mining and society. Provides a comprehensive, practical look at the concepts and techniques needed to get the most out of your data
Data Mining: Concepts, Methods and Applications in Management and Engineering Design (Decision Engineering)
by Jiafu Tang Yong Yin Ikou Kaku Jianming ZhuData Mining introduces in clear and simple ways how to use existing data mining methods to obtain effective solutions for a variety of management and engineering design problems. Data Mining is organised into two parts: the first provides a focused introduction to data mining and the second goes into greater depth on subjects such as customer analysis. It covers almost all managerial activities of a company, including: * supply chain design, * product development, * manufacturing system design, * product quality control, and * preservation of privacy. Incorporating recent developments of data mining that have made it possible to deal with management and engineering design problems with greater efficiency and efficacy, Data Mining presents a number of state-of-the-art topics. It will be an informative source of information for researchers, but will also be a useful reference work for industrial and managerial practitioners.
Data Mining: Concepts, Models, Methods, and Algorithms
by Mehmed KantardzicPresents the latest techniques for analyzing and extracting information from large amounts of data in high-dimensional data spaces The revised and updated third edition of Data Mining contains in one volume an introduction to a systematic approach to the analysis of large data sets that integrates results from disciplines such as statistics, artificial intelligence, data bases, pattern recognition, and computer visualization. Advances in deep learning technology have opened an entire new spectrum of applications. The author—a noted expert on the topic—explains the basic concepts, models, and methodologies that have been developed in recent years. This new edition introduces and expands on many topics, as well as providing revised sections on software tools and data mining applications. Additional changes include an updated list of references for further study, and an extended list of problems and questions that relate to each chapter.This third edition presents new and expanded information that: • Explores big data and cloud computing • Examines deep learning • Includes information on convolutional neural networks (CNN) • Offers reinforcement learning • Contains semi-supervised learning and S3VM • Reviews model evaluation for unbalanced data Written for graduate students in computer science, computer engineers, and computer information systems professionals, the updated third edition of Data Mining continues to provide an essential guide to the basic principles of the technology and the most recent developments in the field.
Data Mining: Concepts, Models, Methods, and Algorithms (Second Edition)
by Mehmed KantardzicThis book reviews state-of-the-art methodologies and techniques for analyzing enormous quantities of raw data in high-dimensional data spaces, to extract new information for decision making. The goal of this book is to provide a single introductory source, organized in a systematic way, in which we could direct the readers in analysis of large data sets, through the explanation of basic concepts, models and methodologies developed in recent decades. If you are an instructor or professor and would like to obtain instructor's materials, please visit http://booksupport.wiley.com If you are an instructor or professor and would like to obtain a solutions manual, please send an email to: pressbooks@ieee.org
Data Mining: Modelle und Algorithmen intelligenter Datenanalyse (Computational Intelligence)
by Thomas A. RunklerDieses Lehrbuch behandelt die wichtigsten Methoden zur Erkennung und Extraktion von ,,Wissen" aus numerischen und nicht-numerischen Datenbanken in Technik und Wirtschaft. Der Autor vermittelt einen kompakten und zugleich fundierten #65533;berblick #65533;ber die verschiedenen Methoden sowie deren Zielsetzungen und Eigenschaften. Dadurch werden Leser bef#65533;higt, Data Mining eigenst#65533;ndig anzuwenden.
Data Mining: Technologies, Techniques, Tools, and Trends
by Bhavani ThuraisinghamFocusing on a data-centric perspective, this book provides a complete overview of data mining: its uses, methods, current technologies, commercial products, and future challenges.Three parts divide Data Mining:Part I describes technologies for data mining - database systems, warehousing, machine learning, visualization, decision sup
Data Mining: The Textbook (Chapman And Hall/crc Data Mining And Knowledge Discovery Ser. #31)
by Charu C. AggarwalThis textbook explores the different aspects of data mining from the fundamentals to the complex data types and their applications, capturing the wide diversity of problem domains for data mining issues. It goes beyond the traditional focus on data mining problems to introduce advanced data types such as text, time series, discrete sequences, spatial data, graph data, and social networks. Until now, no single book has addressed all these topics in a comprehensive and integrated way. The chapters of this book fall into one of three categories: Fundamental chapters: Data mining has four main problems, which correspond to clustering, classification, association pattern mining, and outlier analysis. These chapters comprehensively discuss a wide variety of methods for these problems. Domain chapters: These chapters discuss the specific methods used for different domains of data such as text data, time-series data, sequence data, graph data, and spatial data. Application chapters: These chapters study important applications such as stream mining, Web mining, ranking, recommendations, social networks, and privacy preservation. The domain chapters also have an applied flavor. Appropriate for both introductory and advanced data mining courses, Data Mining: The Textbook balances mathematical details and intuition. It contains the necessary mathematical details for professors and researchers, but it is presented in a simple and intuitive style to improve accessibility for students and industrial practitioners (including those with a limited mathematical background). Numerous illustrations, examples, and exercises are included, with an emphasis on semantically interpretable examples. Praise for Data Mining: The Textbook - "As I read through this book, I have already decided to use it in my classes. This is a book written by an outstanding researcher who has made fundamental contributions to data mining, in a way that is both accessible and up to date. The book is complete with theory and practical use cases. It's a must-have for students and professors alike!" -- Qiang Yang, Chair of Computer Science and Engineering at Hong Kong University of Science and Technology "This is the most amazing and comprehensive text book on data mining. It covers not only the fundamental problems, such as clustering, classification, outliers and frequent patterns, and different data types, including text, time series, sequences, spatial data and graphs, but also various applications, such as recommenders, Web, social network and privacy. It is a great book for graduate students and researchers as well as practitioners. " -- Philip S. Yu, UIC Distinguished Professor and Wexler Chair in Information Technology at University of Illinois at Chicago
Data Mining: Theories, Algorithms, and Examples (Human Factors And Ergonomics Ser.)
by Nong YeNew technologies have enabled us to collect massive amounts of data in many fields. However, our pace of discovering useful information and knowledge from these data falls far behind our pace of collecting the data. Data Mining: Theories, Algorithms, and Examples introduces and explains a comprehensive set of data mining algorithms from various dat
Data Mining: Theory, Methodology, Techniques, And Applications (Lecture Notes in Computer Science #3755)
by David Stirling Lin Liu Yee Ling Boo Lianhua Chi Kok-Leong Ong Graham WilliamsThis book constitutes the refereed proceedings of the 15th Australasian Conference on Data Mining, AusDM 2017, held in Melbourne, VIC, Australia, in August 2017.The 17 revised full papers presented together with 11 research track papers and 6 application track papers were carefully reviewed and selected from 31 submissions. The papers are organized in topical sections on clustering and classification; big data; time series; outlier detection and applications; social media and applications.
Data Modeling for Azure Data Services: Implement professional data design and structures in Azure
by Peter ter BraakeChoose the right Azure data service and correct model design for successful implementation of your data model with the help of this hands-on guideKey FeaturesDesign a cost-effective, performant, and scalable database in AzureChoose and implement the most suitable design for a databaseDiscover how your database can scale with growing data volumes, concurrent users, and query complexityBook DescriptionData is at the heart of all applications and forms the foundation of modern data-driven businesses. With the multitude of data-related use cases and the availability of different data services, choosing the right service and implementing the right design becomes paramount to successful implementation. Data Modeling for Azure Data Services starts with an introduction to databases, entity analysis, and normalizing data. The book then shows you how to design a NoSQL database for optimal performance and scalability and covers how to provision and implement Azure SQL DB, Azure Cosmos DB, and Azure Synapse SQL Pool. As you progress through the chapters, you'll learn about data analytics, Azure Data Lake, and Azure SQL Data Warehouse and explore dimensional modeling, data vault modeling, along with designing and implementing a Data Lake using Azure Storage. You'll also learn how to implement ETL with Azure Data Factory. By the end of this book, you'll have a solid understanding of which Azure data services are the best fit for your model and how to implement the best design for your solution.What you will learnModel relational database using normalization, dimensional, or Data Vault modelingProvision and implement Azure SQL DB and Azure Synapse SQL PoolsDiscover how to model a Data Lake and implement it using Azure StorageModel a NoSQL database and provision and implement an Azure Cosmos DBUse Azure Data Factory to implement ETL/ELT processesCreate a star schema model using dimensional modelingWho this book is forThis book is for business intelligence developers and consultants who work on (modern) cloud data warehousing and design and implement databases. Beginner-level knowledge of cloud data management is expected.
Data Modeling with Microsoft Excel: Model and analyze data using Power Pivot, DAX, and Cube functions
by Bernard Obeng BoatengSave time analyzing volumes of data using a structured method to extract, model, and create insights from your dataKey FeaturesAcquire expertise in using Excel’s Data Model and Power Pivot to connect and analyze multiple sources of dataCreate key performance indicators for decision making using DAX and Cube functionsApply your knowledge of Data Model to build an interactive dashboard that delivers key insights to your usersPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionMicrosoft Excel's BI solutions have evolved, offering users more flexibility and control over analyzing data directly in Excel. Features like PivotTables, Data Model, Power Query, and Power Pivot empower Excel users to efficiently get, transform, model, aggregate, and visualize data. Data Modeling with Microsoft Excel offers a practical way to demystify the use and application of these tools using real-world examples and simple illustrations. This book will introduce you to the world of data modeling in Excel, as well as definitions and best practices in data structuring for both normalized and denormalized data. The next set of chapters will take you through the useful features of Data Model and Power Pivot, helping you get to grips with the types of schemas (snowflake and star) and create relationships within multiple tables. You’ll also understand how to create powerful and flexible measures using DAX and Cube functions. By the end of this book, you’ll be able to apply the acquired knowledge in real-world scenarios and build an interactive dashboard that will help you make important decisions. Note: To access the supplemental material, subscribers should purchase a print copy of the book. The ebook can be accessed through the QR code or link provided inside the Print book. Proof of purchase is mandatory to access the ebook.What you will learnImplement the concept of data modeling within and beyond ExcelGet, transform, model, aggregate, and visualize data with Power QueryUnderstand best practices for data structuring in MS ExcelBuild powerful measures using DAX from the Data ModelGenerate flexible calculations using Cube functionsDesign engaging dashboards for your usersWho this book is forThis book is for Excel users looking for hands-on and effective methods to manage and analyze large volumes of data within Microsoft Excel using Power Pivot. Whether you’re new or already familiar with Excel’s data analytics tools, this book will give you further insights on how you can apply Power Pivot, Data Model, DAX measures, and Cube functions to save time on routine data management tasks. An understanding of Excel’s features like tables, PivotTable, and some basic aggregating functions will be helpful but not necessary to make the most of this book.
Data Modeling with Microsoft Power BI: Self-Service and Enterprise Data Warehouse with Power BI
by Markus Ehrenmueller-JensenData modeling is the single most overlooked feature in Power BI Desktop, yet it's what sets Power BI apart from other tools on the market. This practical book serves as your fast-forward button for data modeling with Power BI, Analysis Services tabular, and SQL databases. It serves as a starting point for data modeling, as well as a handy refresher.Author Markus Ehrenmueller-Jensen, founder of Savory Data, shows you the basic concepts of Power BI's semantic model with hands-on examples in DAX, Power Query, and T-SQL. If you're looking to build a data warehouse layer, chapters with T-SQL examples will get you started. You'll begin with simple steps and gradually solve more complex problems.This book shows you how to:Normalize and denormalize with DAX, Power Query, and T-SQLApply best practices for calculations, flags and indicators, time and date, role-playing dimensions and slowly changing dimensionsSolve challenges such as binning, budget, localized models, composite models, and key value with DAX, Power Query, and T-SQLDiscover and tackle performance issues by applying solutions in DAX, Power Query, and T-SQLWork with tables, relations, set operations, normal forms, dimensional modeling, and ETL
Data Modeling with SAP BW/4HANA 2.0: Implementing Agile Data Models Using Modern Modeling Concepts
by Konrad ZaleskiGain practical guidance for implementing data models on the SAP BW/4HANA platform using modern modeling concepts. You will walk through the various modeling scenarios such as exposing HANA tables and views through BW/4HANA, creating virtual and hybrid data models, and integrating SAP and non-SAP data into a single data model. Data Modeling with SAP BW/4HANA 2.0 gives you the skills you need to use the new SAP BW/HANA features and objects, covers modern modelling concepts, and equips you with the practical knowledge of how to use the best of the HANA and BW/4HANA worlds. What You Will Learn Discover the new modeling features in SAP BW/4HANA Combine SAP HANA and SAP BW/4HANA artifacts Leverage virtualization when designing and building data models Build hybrid data models combining InfoObject, OpenODS, and a field-based approach Integrate SAP and non-SAP data into single model Who This Book Is For BI consultants, architects, developers, and analysts working in the SAP BW/4HANA environment.
Data Modeling with Tableau: A practical guide to building data models using Tableau Prep and Tableau Desktop
by Kirk MunroeSave time analyzing volumes of data using best practices to extract, model, and create insights from your dataKey FeaturesMaster best practices in data modeling with Tableau Prep Builder and Tableau DesktopApply Tableau Server and Cloud to create and extend data modelsBuild organizational data models based on data and content governance best practicesBook DescriptionTableau is unlike most other BI platforms that have a single data modeling tool and enterprise data model (for example, LookML from Google's Looker). That doesn't mean Tableau doesn't have enterprise data governance; it is both robust and very flexible. This book will help you build a data-driven organization with the proper use of Tableau governance models.Data Modeling with Tableau is an extensive guide, complete with step-by-step explanations of essential concepts, practical examples, and hands-on exercises. As you progress through the chapters, you will learn the role that Tableau Prep Builder and Tableau Desktop each play in data modeling. You'll also explore the components of Tableau Server and Cloud that make data modeling more robust, secure, and performant. Moreover, by extending data models for Ask and Explain Data, you'll gain the knowledge required to extend analytics to more people in their organizations, leading to better data-driven decisions. Finally, this book will get into the entire Tableau stack and get the techniques required to build the right level of governance into Tableau data models for the right use cases.By the end of this Tableau book, you'll have a firm understanding of how to leverage data modeling in Tableau to benefit your organization.What you will learnShowcase Tableau published data sources and embedded connectionsApply Ask Data in data cataloging and natural language queryExhibit features of Tableau Prep Builder with hands-on exercisesModel data with Tableau Desktop through examplesFormulate a governed data strategy using Tableau Server and CloudOptimize data models for Ask and Explain DataWho this book is forThis book is for data analysts and business analysts who are looking to expand their data skills, offering a broad foundation to build better data models in Tableau for easier analysis and better query performance.It will also benefit individuals responsible for making trusted and secure data available to their organization through Tableau, such as data stewards and others who work to take enterprise data and make it more accessible to business analysts.
Data Parallel C++: Mastering DPC++ for Programming of Heterogeneous Systems using C++ and SYCL
by James Reinders James Brodman Ben Ashbaugh Michael Kinsner John Pennycook Xinmin TianLearn how to accelerate C++ programs using data parallelism. This open access book enables C++ programmers to be at the forefront of this exciting and important new development that is helping to push computing to new levels. It is full of practical advice, detailed explanations, and code examples to illustrate key topics. Data parallelism in C++ enables access to parallel resources in a modern heterogeneous system, freeing you from being locked into any particular computing device. Now a single C++ application can use any combination of devices—including GPUs, CPUs, FPGAs and AI ASICs—that are suitable to the problems at hand.This book begins by introducing data parallelism and foundational topics for effective use of the SYCL standard from the Khronos Group and Data Parallel C++ (DPC++), the open source compiler used in this book. Later chapters cover advanced topics including error handling, hardware-specific programming, communication and synchronization, and memory model considerations.Data Parallel C++ provides you with everything needed to use SYCL for programming heterogeneous systems. What You'll Learn Accelerate C++ programs using data-parallel programmingTarget multiple device types (e.g. CPU, GPU, FPGA)Use SYCL and SYCL compilers Connect with computing’s heterogeneous future via Intel’s oneAPI initiative Who This Book Is For Those new data-parallel programming and computer programmers interested in data-parallel programming using C++.
Data Parallel C++: Programming Accelerated Systems Using C++ and SYCL
by James Reinders James Brodman Ben Ashbaugh Michael Kinsner John Pennycook Xinmin Tian"This book, now in is second edition, is the premier resource to learn SYCL 2020 and is the ONLY book you need to become part of this community." Erik Lindahl, GROMACS and Stockholm UniversityLearn how to accelerate C++ programs using data parallelism and SYCL.This open access book enables C++ programmers to be at the forefront of this exciting and important development that is helping to push computing to new levels. This updated second edition is full of practical advice, detailed explanations, and code examples to illustrate key topics. SYCL enables access to parallel resources in modern accelerated heterogeneous systems. Now, a single C++ application can use any combination of devices–including GPUs, CPUs, FPGAs, and ASICs–that are suitable to the problems at hand. This book teaches data-parallel programming using C++ with SYCL and walks through everything needed to program accelerated systems. The book begins by introducing data parallelism and foundational topics for effective use of SYCL. Later chapters cover advanced topics, including error handling, hardware-specific programming, communication and synchronization, and memory model considerations. All source code for the examples used in this book is freely available on GitHub. The examples are written in modern SYCL and are regularly updated to ensure compatibility with multiple compilers. What You Will Learn Accelerate C++ programs using data-parallel programmingUse SYCL and C++ compilers that support SYCLWrite portable code for accelerators that is vendor and device agnosticOptimize code to improve performance for specific acceleratorsBe poised to benefit as new accelerators appear from many vendorsWho This Book Is For New data-parallel programming and computer programmers interested in data-parallel programming using C++This is an open access book.
Data Patterns
by Microsoft CorporationGet expert guidance on using patterns to expedite the design and development of data services in an enterprise business solution. Patterns provide a common vocabulary and taxonomy for database designers, developers, and architects to describe solutions concisely. Each pattern contains a simple mechanism for solving a commonly recurring technical challenge and enables the reuse of key architectural, design, and implementation decisions. While each pattern can be understood and applied alone, you can also combine these patterns together to simplify the development of complex systems. Software design professionals have increasingly recognized the value of patterns as a language for sharing design experiences and improving the reliability and productivity of their solutions. This book embraces and extends the work of the growing patterns community by showing how to use patterns to solve data problems within the enterprise with Microsoft products and technologies. These patterns address the need to create the database designs and the data services that exist invisibly to the applications that use the data; in other words, the data and services that exist within the data ecosystem. This reference contains a catalog of 12 data patterns, including examples of implementations that use Microsoft SQL Server. All PATTERNS & PRACTICES guides are reviewed and approved by Microsoft engineering teams, consultants, partners, and customers--delivering accurate, real-world information that's been technically validated and tested.
Data Pipelines Pocket Reference: Moving And Processing Data For Analytics
by James DensmoreData pipelines are the foundation for success in data analytics. Moving data from numerous diverse sources and transforming it to provide context is the difference between having data and actually gaining value from it. This pocket reference defines data pipelines and explains how they work in today's modern data stack.You'll learn common considerations and key decision points when implementing pipelines, such as batch versus streaming data ingestion and build versus buy. This book addresses the most common decisions made by data professionals and discusses foundational concepts that apply to open source frameworks, commercial products, and homegrown solutions.You'll learn:What a data pipeline is and how it worksHow data is moved and processed on modern data infrastructure, including cloud platformsCommon tools and products used by data engineers to build pipelinesHow pipelines support analytics and reporting needsConsiderations for pipeline maintenance, testing, and alerting
Data Pipelines with Apache Airflow
by Julian de Ruiter Bas HarenslakData Pipelines with Apache Airflow teaches you how to build and maintain effective data pipelines.Summary A successful pipeline moves data efficiently, minimizing pauses and blockages between tasks, keeping every process along the way operational. Apache Airflow provides a single customizable environment for building and managing data pipelines, eliminating the need for a hodgepodge collection of tools, snowflake code, and homegrown processes. Using real-world scenarios and examples, Data Pipelines with Apache Airflow teaches you how to simplify and automate data pipelines, reduce operational overhead, and smoothly integrate all the technologies in your stack. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Data pipelines manage the flow of data from initial collection through consolidation, cleaning, analysis, visualization, and more. Apache Airflow provides a single platform you can use to design, implement, monitor, and maintain your pipelines. Its easy-to-use UI, plug-and-play options, and flexible Python scripting make Airflow perfect for any data management task. About the book Data Pipelines with Apache Airflow teaches you how to build and maintain effective data pipelines. You&’ll explore the most common usage patterns, including aggregating multiple data sources, connecting to and from data lakes, and cloud deployment. Part reference and part tutorial, this practical guide covers every aspect of the directed acyclic graphs (DAGs) that power Airflow, and how to customize them for your pipeline&’s needs. What's inside Build, test, and deploy Airflow pipelines as DAGs Automate moving and transforming data Analyze historical datasets using backfilling Develop custom components Set up Airflow in production environments About the reader For DevOps, data engineers, machine learning engineers, and sysadmins with intermediate Python skills. About the author Bas Harenslak and Julian de Ruiter are data engineers with extensive experience using Airflow to develop pipelines for major companies. Bas is also an Airflow committer. Table of Contents PART 1 - GETTING STARTED 1 Meet Apache Airflow 2 Anatomy of an Airflow DAG 3 Scheduling in Airflow 4 Templating tasks using the Airflow context 5 Defining dependencies between tasks PART 2 - BEYOND THE BASICS 6 Triggering workflows 7 Communicating with external systems 8 Building custom components 9 Testing 10 Running tasks in containers PART 3 - AIRFLOW IN PRACTICE 11 Best practices 12 Operating Airflow in production 13 Securing Airflow 14 Project: Finding the fastest way to get around NYC PART 4 - IN THE CLOUDS 15 Airflow in the clouds 16 Airflow on AWS 17 Airflow on Azure 18 Airflow in GCP
Data Plane Development Kit (DPDK): A Software Optimization Guide to the User Space-Based Network Applications
by Heqing ZhuThis book brings together the insights and practical experience of some of the most experienced Data Plane Development Kit (DPDK) technical experts, detailing the trend of DPDK, data packet processing, hardware acceleration, packet processing and virtualization, as well as the practical application of DPDK in the fields of SDN, NFV, and network storage. The book also devotes many chunks to exploring various core software algorithms, the advanced optimization methods adopted in DPDK, detailed practical experience, and the guides on how to use DPDK.
Data Points
by Nathan YauA fresh look at visualization from the author of Visualize ThisWhether it's statistical charts, geographic maps, or the snappy graphical statistics you see on your favorite news sites, the art of data graphics or visualization is fast becoming a movement of its own. In Data Points: Visualization That Means Something, author Nathan Yau presents an intriguing complement to his bestseller Visualize This, this time focusing on the graphics side of data analysis. Using examples from art, design, business, statistics, cartography, and online media, he explores both standard-and not so standard-concepts and ideas about illustrating data. Shares intriguing ideas from Nathan Yau, author of Visualize This and creator of flowingdata.com, with over 66,000 subscribersFocuses on visualization, data graphics that help viewers see trends and patterns they might not otherwise see in a tableIncludes examples from the author's own illustrations, as well as from professionals in statistics, art, design, business, computer science, cartography, and moreExamines standard rules across all visualization applications, then explores when and where you can break those rulesCreate visualizations that register at all levels, with Data Points: Visualization That Means Something.
Data Preprocessing in Data Mining (Intelligent Systems Reference Library #72)
by Francisco Herrera Salvador García Julián LuengoData Preprocessing for Data Mining addresses one of the most important issues within the well-known Knowledge Discovery from Data process. Data directly taken from the source will likely have inconsistencies, errors or most importantly, it is not ready to be considered for a data mining process. Furthermore, the increasing amount of data in recent science, industry and business applications, calls to the requirement of more complex tools to analyze it. Thanks to data preprocessing, it is possible to convert the impossible into possible, adapting the data to fulfill the input demands of each data mining algorithm. Data preprocessing includes the data reduction techniques, which aim at reducing the complexity of the data, detecting or removing irrelevant and noisy elements from the data. This book is intended to review the tasks that fill the gap between the data acquisition from the source and the data mining process. A comprehensive look from a practical point of view, including basic concepts and surveying the techniques proposed in the specialized literature, is given. Each chapter is a stand-alone guide to a particular data preprocessing topic, from basic concepts and detailed descriptions of classical algorithms, to an incursion of an exhaustive catalog of recent developments. The in-depth technical descriptions make this book suitable for technical professionals, researchers, senior undergraduate and graduate students in data science, computer science and engineering.
Data Preprocessing with Python for Absolute Beginners: Take your first steps in data preparation with Python
by AI Sciences OUThis book is dedicated to data preparation and explains how to perform different data preparation techniques on various datasets using different data preparation libraries written in the Python programming language.Key FeaturesA crash course in Python to fill any gaps in prerequisite knowledge and a solid foundation on which to build your new skillsA complete data preparation pipeline for your guided practiceThree real-world projects covering each major task to cement your learned skills in data preparation, classification, and regressionBook DescriptionThe book follows a straightforward approach. It is divided into nine chapters. Chapter 1 introduces the basic concept of data preparation and installation steps for the software that we will need to perform data preparation in this book. Chapter 1 also contains a crash course on Python, followed by a brief overview of different data types in Chapter 2. You will then learn how to handle missing values in the data, while the categorical encoding of numeric data is explained in Chapter 4.The second half of the course presents data discretization and describes the handling of outliers' process. Chapter 7 demonstrates how to scale features in the dataset. Subsequent chapters teach you to handle mixed and DateTime data type, balance data, and practice resampling. A full data preparation final project is also available at the end of the book.Different types of data preprocessing techniques have been explained theoretically, followed by practical examples in each chapter. Each chapter also contains an exercise that students can use to evaluate their understanding of the chapter's concepts. By the end of this course, you will have built a solid working knowledge in data preparation--the first steps to any data science or machine learning career and an essential skillset for any aspiring developer.The code bundle for this course is available at https://www.aispublishing.net/book-data-preprocessingWhat you will learnExplore different libraries for data preparationUnderstand data typesHandle missing dataEncode categorical dataDiscretize dataLearn to handle outliersPractice feature scalingHandle mixed and DateTime variables and imbalanced datasetsEmploy your new skills to complete projects in data preparation, classification, and regressionWho this book is forIn addition to beginners in data preparation with Python, this book can also be used as a reference manual by intermediate and experienced programmers. It contains data preprocessing code samples using multiple data visualization libraries.
Data Privacy Games
by Yi Qian Chunxiao Jiang Lei Xu Yong RenWith the growing popularity of “big data”, the potential value of personal data has attracted more and more attention. Applications built on personal data can create tremendous social and economic benefits. Meanwhile, they bring serious threats to individual privacy. The extensive collection, analysis and transaction of personal data make it difficult for an individual to keep the privacy safe. People now show more concerns about privacy than ever before. How to make a balance between the exploitation of personal information and the protection of individual privacy has become an urgent issue.In this book, the authors use methodologies from economics, especially game theory, to investigate solutions to the balance issue. They investigate the strategies of stakeholders involved in the use of personal data, and try to find the equilibrium. The book proposes a user-role based methodology to investigate the privacy issues in data mining, identifying four different types of users, i.e. four user roles, involved in data mining applications. For each user role, the authors discuss its privacy concerns and the strategies that it can adopt to solve the privacy problems.The book also proposes a simple game model to analyze the interactions among data provider, data collector and data miner. By solving the equilibria of the proposed game, readers can get useful guidance on how to deal with the trade-off between privacy and data utility. Moreover, to elaborate the analysis on data collector’s strategies, the authors propose a contract model and a multi-armed bandit model respectively. The authors discuss how the owners of data (e.g. an individual or a data miner) deal with the trade-off between privacy and utility in data mining. Specifically, they study users’ strategies in collaborative filtering based recommendation system and distributed classification system. They built game models to formulate the interactions among data owners, and propose learning algorithms to find the equilibria.