Data Mining and Big Data

Data Mining and Big Data
Author: Ying Tan,Yuhui Shi,Qirong Tang
Publsiher: Springer
Total Pages: 799
Release: 2018-06-09
ISBN: 3319938037
Category: Computers
Language: EN, FR, DE, ES & NL

Data Mining and Big Data Book Excerpt:

This book constitutes the refereed proceedings of the Third International Conference on Data Mining and Big Data, DMBD 2018, held in Shanghai, China, in June 2018. The 74 papers presented in this volume were carefully reviewed and selected from 126 submissions. They are organized in topical sections named: database, data preprocessing, matrix factorization, data analysis, visualization, visibility analysis, clustering, prediction, classification, pattern discovery, text mining and knowledge management, recommendation system in social media, deep learning, big data, Industry 4.0, practical applications

Predictive Analytics Data Mining and Big Data

Predictive Analytics  Data Mining and Big Data
Author: S. Finlay
Publsiher: Springer
Total Pages: 260
Release: 2014-07-01
ISBN: 1137379286
Category: Business & Economics
Language: EN, FR, DE, ES & NL

Predictive Analytics Data Mining and Big Data Book Excerpt:

This in-depth guide provides managers with a solid understanding of data and data trends, the opportunities that it can offer to businesses, and the dangers of these technologies. Written in an accessible style, Steven Finlay provides a contextual roadmap for developing solutions that deliver benefits to organizations.

Big Data Mining and Complexity

Big Data Mining and Complexity
Author: Brian C. Castellani,Rajeev Rajaram
Publsiher: SAGE
Total Pages: 180
Release: 2022-03-26
ISBN: 1529711010
Category: Reference
Language: EN, FR, DE, ES & NL

Big Data Mining and Complexity Book Excerpt:

This book offers a much needed critical introduction to data mining and ‘big data’. Supported by multiple case studies and examples, the authors provide everything needed to explore, evaluate and review big data concepts and techniques.

Big Data Mining and Complexity

Big Data Mining and Complexity
Author: Brian C. Castellani,Rajeev Rajaram
Publsiher: SAGE
Total Pages: 180
Release: 2022-03-01
ISBN: 1529710995
Category: Social Science
Language: EN, FR, DE, ES & NL

Big Data Mining and Complexity Book Excerpt:

This book offers a much needed critical introduction to data mining and ‘big data’. Supported by multiple case studies and examples, the authors provide: Digestible overviews of key terms and concepts relevant to using social media data in quantitative research. A critical review of data mining and ‘big data’ from a complexity science perspective, including its future potential and limitations A practical exploration of the challenges of putting together and managing a ‘big data’ database An evaluation of the core mathematical and conceptual frameworks, grounded in a case-based computational modeling perspective, which form the foundations of all data mining techniques Part of The SAGE Quantitative Research Kit, this book will give you the know-how and confidence needed to succeed on your quantitative research journey.

Big Data Mining and Analytics

Big Data  Mining  and Analytics
Author: Stephan Kudyba
Publsiher: CRC Press
Total Pages: 325
Release: 2014-03-12
ISBN: 1466568712
Category: Computers
Language: EN, FR, DE, ES & NL

Big Data Mining and Analytics Book Excerpt:

There is an ongoing data explosion transpiring that will make previous creations, collections, and storage of data look trivial. Big Data, Mining, and Analytics: Components of Strategic Decision Making ties together big data, data mining, and analytics to explain how readers can leverage them to extract valuable insights from their data. Facilitati

Social Big Data Mining

Social Big Data Mining
Author: Hiroshi Ishikawa
Publsiher: CRC Press
Total Pages: 268
Release: 2015-03-25
ISBN: 1498710948
Category: Computers
Language: EN, FR, DE, ES & NL

Social Big Data Mining Book Excerpt:

This book focuses on the basic concepts and the related technologies of data mining for social medial. Topics include: big data and social data, data mining for making a hypothesis, multivariate analysis for verifying the hypothesis, web mining and media mining, natural language processing, social big data applications, and scalability. It explains analytical techniques such as modeling, data mining, and multivariate analysis for social big data. This book is different from other similar books in that presents the overall picture of social big data from fundamental concepts to applications while standing on academic bases.

Data Mining and Big Data

Data Mining and Big Data
Author: Ying Tan,Yuhui Shi
Publsiher: Springer
Total Pages: 340
Release: 2019-07-26
ISBN: 9789813295629
Category: Computers
Language: EN, FR, DE, ES & NL

Data Mining and Big Data Book Excerpt:

This book constitutes the refereed proceedings of the 4th International Conference on Data Mining and Big Data, DMBD 2019, held in Chiang Mai, Thailand, in July 2019. The 26 fill papers and 8 short papers presented in this volume were carefully reviewed and selected from 79 submissions. They are organized in topical sections named: data analysis; prediction; clustering; classification; mining pattern; mining tasks.

Metaheuristics for Big Data

Metaheuristics for Big Data
Author: Clarisse Dhaenens,Laetitia Jourdan
Publsiher: John Wiley & Sons
Total Pages: 212
Release: 2016-08-16
ISBN: 1119347602
Category: Computers
Language: EN, FR, DE, ES & NL

Metaheuristics for Big Data Book Excerpt:

Big Data is a new field, with many technological challenges to be understood in order to use it to its full potential. These challenges arise at all stages of working with Big Data, beginning with data generation and acquisition. The storage and management phase presents two critical challenges: infrastructure, for storage and transportation, and conceptual models. Finally, to extract meaning from Big Data requires complex analysis. Here the authors propose using metaheuristics as a solution to these challenges; they are first able to deal with large size problems and secondly flexible and therefore easily adaptable to different types of data and different contexts. The use of metaheuristics to overcome some of these data mining challenges is introduced and justified in the first part of the book, alongside a specific protocol for the performance evaluation of algorithms. An introduction to metaheuristics follows. The second part of the book details a number of data mining tasks, including clustering, association rules, supervised classification and feature selection, before explaining how metaheuristics can be used to deal with them. This book is designed to be self-contained, so that readers can understand all of the concepts discussed within it, and to provide an overview of recent applications of metaheuristics to knowledge discovery problems in the context of Big Data.

Big Data Mining for Climate Change

Big Data Mining for Climate Change
Author: Zhihua Zhang,Jianping Li
Publsiher: Elsevier
Total Pages: 344
Release: 2019-11-20
ISBN: 0128187042
Category: Computers
Language: EN, FR, DE, ES & NL

Big Data Mining for Climate Change Book Excerpt:

Climate change mechanisms, impacts, risks, mitigation, adaption, and governance are widely recognized as the biggest, most interconnected problem facing humanity. Big Data Mining for Climate Change addresses one of the fundamental issues facing scientists of climate or the environment: how to manage the vast amount of information available and analyse it. The resulting integrated and interdisciplinary big data mining approaches are emerging, partially with the help of the United Nation’s big data climate challenge, some of which are recommended widely as new approaches for climate change research. Big Data Mining for Climate Change delivers a rich understanding of climate-related big data techniques and highlights how to navigate huge amount of climate data and resources available using big data applications. It guides future directions and will boom big-data-driven researches on modeling, diagnosing and predicting climate change and mitigating related impacts. This book mainly focuses on climate network models, deep learning techniques for climate dynamics, automated feature extraction of climate variability, and sparsification of big climate data. It also includes a revelatory exploration of big-data-driven low-carbon economy and management. Its content provides cutting-edge knowledge for scientists and advanced students studying climate change from various disciplines, including atmospheric, oceanic and environmental sciences; geography, ecology, energy, economics, management, engineering, and public policy. Provides a step-by-step guide for applying big data mining tools to climate and environmental research Presents a comprehensive review of theory and algorithms of big data mining for climate change Includes current research in climate and environmental science as it relates to using big data algorithms

Proceedings of 5th International Conference on Big Data Analysis and Data Mining 2018

Proceedings of 5th International Conference on Big Data Analysis and Data Mining 2018
Author: ConferenceSeries
Publsiher: ConferenceSeries
Total Pages: 89
Release: 2022
ISBN: 1928374650XXX
Category: Electronic Book
Language: EN, FR, DE, ES & NL

Proceedings of 5th International Conference on Big Data Analysis and Data Mining 2018 Book Excerpt:

June 20-22, 2018 Rome, Italy Key Topics : Data Mining Applications in Science, Engineering, Healthcare and Medicine, Big Data in Nursing Research, Data Mining and Machine Learning, Big Data Analytics, Optimization and Big Data, Big data technologies, Big Data algorithm, Big Data Applications, Forecasting from Big Data, Data Mining Methods and Algorithms, Artificial Intelligence, Data privacy and ethics, Data Warehousing, Data Mining Tools and Software, Data Mining Tasks and Processes, Data Mining analysis, Cloud computing, Internet of things (IOT), Social network analysis, Complexity and algorithms, Business Analytics, Open data, New visualization techniques, Search and data mining, Frequent pattern mining, Clustering, Others

Security and Privacy Trends in Cloud Computing and Big Data

Security and Privacy Trends in Cloud Computing and Big Data
Author: Muhammad Imran Tariq,Valentina Emilia Balas,Shahzadi Tayyaba
Publsiher: CRC Press
Total Pages: 232
Release: 2022-06-09
ISBN: 1000583708
Category: Computers
Language: EN, FR, DE, ES & NL

Security and Privacy Trends in Cloud Computing and Big Data Book Excerpt:

It is essential for an organization to know before involving themselves in cloud computing and big data, what are the key security requirements for applications and data processing. Big data and cloud computing are integrated together in practice. Cloud computing offers massive storage, high computation power, and distributed capability to support processing of big data. In such an integrated environment the security and privacy concerns involved in both technologies become combined. This book discusses these security and privacy issues in detail and provides necessary insights into cloud computing and big data integration. It will be useful in enhancing the body of knowledge concerning innovative technologies offered by the research community in the area of cloud computing and big data. Readers can get a better understanding of the basics of cloud computing, big data, and security mitigation techniques to deal with current challenges as well as future research opportunities.

Big Data Data Mining and Machine Learning

Big Data  Data Mining  and Machine Learning
Author: Jared Dean
Publsiher: John Wiley & Sons
Total Pages: 288
Release: 2014-05-27
ISBN: 1118618041
Category: Computers
Language: EN, FR, DE, ES & NL

Big Data Data Mining and Machine Learning Book Excerpt:

With big data analytics comes big insights into profitability Big data is big business. But having the data and the computational power to process it isn't nearly enough to produce meaningful results. Big Data, Data Mining, and Machine Learning: Value Creation for Business Leaders and Practitioners is a complete resource for technology and marketing executives looking to cut through the hype and produce real results that hit the bottom line. Providing an engaging, thorough overview of the current state of big data analytics and the growing trend toward high performance computing architectures, the book is a detail-driven look into how big data analytics can be leveraged to foster positive change and drive efficiency. With continued exponential growth in data and ever more competitive markets, businesses must adapt quickly to gain every competitive advantage available. Big data analytics can serve as the linchpin for initiatives that drive business, but only if the underlying technology and analysis is fully understood and appreciated by engaged stakeholders. This book provides a view into the topic that executives, managers, and practitioners require, and includes: A complete overview of big data and its notable characteristics Details on high performance computing architectures for analytics, massively parallel processing (MPP), and in-memory databases Comprehensive coverage of data mining, text analytics, and machine learning algorithms A discussion of explanatory and predictive modeling, and how they can be applied to decision-making processes Big Data, Data Mining, and Machine Learning provides technology and marketing executives with the complete resource that has been notably absent from the veritable libraries of published books on the topic. Take control of your organization's big data analytics to produce real results with a resource that is comprehensive in scope and light on hyperbole.

Introduction to Algorithms for Data Mining and Machine Learning

Introduction to Algorithms for Data Mining and Machine Learning
Author: Xin-She Yang
Publsiher: Academic Press
Total Pages: 188
Release: 2019-07-15
ISBN: 0128172169
Category: Mathematics
Language: EN, FR, DE, ES & NL

Introduction to Algorithms for Data Mining and Machine Learning Book Excerpt:

Introduction to Algorithms for Data Mining and Machine Learning introduces the essential ideas behind all key algorithms and techniques for data mining and machine learning, along with optimization techniques. Its strong formal mathematical approach, well selected examples, and practical software recommendations help readers develop confidence in their data modeling skills so they can process and interpret data for classification, clustering, curve-fitting and predictions. Masterfully balancing theory and practice, it is especially useful for those who need relevant, well explained, but not rigorous (proofs based) background theory and clear guidelines for working with big data. Presents an informal, theorem-free approach with concise, compact coverage of all fundamental topics Includes worked examples that help users increase confidence in their understanding of key algorithms, thus encouraging self-study Provides algorithms and techniques that can be implemented in any programming language, with each chapter including notes about relevant software packages

Data Mining

Data Mining
Author: Mehmed Kantardzic
Publsiher: John Wiley & Sons
Total Pages: 672
Release: 2019-10-23
ISBN: 1119516072
Category: Computers
Language: EN, FR, DE, ES & NL

Data Mining Book Excerpt:

Presents 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 Approaches for Big Data and Sentiment Analysis in Social Media

Data Mining Approaches for Big Data and Sentiment Analysis in Social Media
Author: Gupta, Brij B.,Perakovi?, Dragan,Abd El-Latif, Ahmed A.,Gupta, Deepak
Publsiher: IGI Global
Total Pages: 313
Release: 2021-12-31
ISBN: 1799884155
Category: Computers
Language: EN, FR, DE, ES & NL

Data Mining Approaches for Big Data and Sentiment Analysis in Social Media Book Excerpt:

Social media sites are constantly evolving with huge amounts of scattered data or big data, which makes it difficult for researchers to trace the information flow. It is a daunting task to extract a useful piece of information from the vast unstructured big data; the disorganized structure of social media contains data in various forms such as text and videos as well as huge real-time data on which traditional analytical methods like statistical approaches fail miserably. Due to this, there is a need for efficient data mining techniques that can overcome the shortcomings of the traditional approaches. Data Mining Approaches for Big Data and Sentiment Analysis in Social Media encourages researchers to explore the key concepts of data mining, such as how they can be utilized on online social media platforms, and provides advances on data mining for big data and sentiment analysis in online social media, as well as future research directions. Covering a range of concepts from machine learning methods to data mining for big data analytics, this book is ideal for graduate students, academicians, faculty members, scientists, researchers, data analysts, social media analysts, managers, and software developers who are seeking to learn and carry out research in the area of data mining for big data and sentiment.

Big Data Analytics Methods

Big Data Analytics Methods
Author: Peter Ghavami
Publsiher: Walter de Gruyter GmbH & Co KG
Total Pages: 254
Release: 2019-12-16
ISBN: 1547401567
Category: Business & Economics
Language: EN, FR, DE, ES & NL

Big Data Analytics Methods Book Excerpt:

Big Data Analytics Methods unveils secrets to advanced analytics techniques ranging from machine learning, random forest classifiers, predictive modeling, cluster analysis, natural language processing (NLP), Kalman filtering and ensembles of models for optimal accuracy of analysis and prediction. More than 100 analytics techniques and methods provide big data professionals, business intelligence professionals and citizen data scientists insight on how to overcome challenges and avoid common pitfalls and traps in data analytics. The book offers solutions and tips on handling missing data, noisy and dirty data, error reduction and boosting signal to reduce noise. It discusses data visualization, prediction, optimization, artificial intelligence, regression analysis, the Cox hazard model and many analytics using case examples with applications in the healthcare, transportation, retail, telecommunication, consulting, manufacturing, energy and financial services industries. This book's state of the art treatment of advanced data analytics methods and important best practices will help readers succeed in data analytics.

Proceedings of 8th Edition of International Conference on Big Data Data Science 2019

Proceedings of 8th Edition of International Conference on Big Data   Data Science 2019
Author: Euroscicon
Publsiher: EuroScicon
Total Pages: 43
Release: 2019-02-24
ISBN: 1928374650XXX
Category: Computers
Language: EN, FR, DE, ES & NL

Proceedings of 8th Edition of International Conference on Big Data Data Science 2019 Book Excerpt:

March 04-05, 2019, Barcelona, Spain Key Topics: Big Data Analytics ,Big Data Algorithms ,Big Data In Bioinformatics ,Data Mining With Big Data ,Visualization In Big Data ,Big Data In Neural Network For Deep Learning ,High Performance Computing For Big Data ,Machine Learning In Data Science ,Open Science In Big Data ,Hadoop Map-Reduce For Analyzing Information ,Regression In Data Science ,Big Data Applications

Resource Management for Big Data Platforms

Resource Management for Big Data Platforms
Author: Florin Pop,Joanna Kołodziej,Beniamino Di Martino
Publsiher: Springer
Total Pages: 516
Release: 2016-10-27
ISBN: 3319448811
Category: Computers
Language: EN, FR, DE, ES & NL

Resource Management for Big Data Platforms Book Excerpt:

Serving as a flagship driver towards advance research in the area of Big Data platforms and applications, this book provides a platform for the dissemination of advanced topics of theory, research efforts and analysis, and implementation oriented on methods, techniques and performance evaluation. In 23 chapters, several important formulations of the architecture design, optimization techniques, advanced analytics methods, biological, medical and social media applications are presented. These chapters discuss the research of members from the ICT COST Action IC1406 High-Performance Modelling and Simulation for Big Data Applications (cHiPSet). This volume is ideal as a reference for students, researchers and industry practitioners working in or interested in joining interdisciplinary works in the areas of intelligent decision systems using emergent distributed computing paradigms. It will also allow newcomers to grasp the key concerns and their potential solutions.

Practical Big Data Analytics

Practical Big Data Analytics
Author: Nataraj Dasgupta
Publsiher: Packt Publishing Ltd
Total Pages: 412
Release: 2018-01-15
ISBN: 1783554401
Category: Computers
Language: EN, FR, DE, ES & NL

Practical Big Data Analytics Book Excerpt:

Get command of your organizational Big Data using the power of data science and analytics Key Features A perfect companion to boost your Big Data storing, processing, analyzing skills to help you take informed business decisions Work with the best tools such as Apache Hadoop, R, Python, and Spark for NoSQL platforms to perform massive online analyses Get expert tips on statistical inference, machine learning, mathematical modeling, and data visualization for Big Data Book Description Big Data analytics relates to the strategies used by organizations to collect, organize and analyze large amounts of data to uncover valuable business insights that otherwise cannot be analyzed through traditional systems. Crafting an enterprise-scale cost-efficient Big Data and machine learning solution to uncover insights and value from your organization's data is a challenge. Today, with hundreds of new Big Data systems, machine learning packages and BI Tools, selecting the right combination of technologies is an even greater challenge. This book will help you do that. With the help of this guide, you will be able to bridge the gap between the theoretical world of technology with the practical ground reality of building corporate Big Data and data science platforms. You will get hands-on exposure to Hadoop and Spark, build machine learning dashboards using R and R Shiny, create web-based apps using NoSQL databases such as MongoDB and even learn how to write R code for neural networks. By the end of the book, you will have a very clear and concrete understanding of what Big Data analytics means, how it drives revenues for organizations, and how you can develop your own Big Data analytics solution using different tools and methods articulated in this book. What you will learn - Get a 360-degree view into the world of Big Data, data science and machine learning - Broad range of technical and business Big Data analytics topics that caters to the interests of the technical experts as well as corporate IT executives - Get hands-on experience with industry-standard Big Data and machine learning tools such as Hadoop, Spark, MongoDB, KDB+ and R - Create production-grade machine learning BI Dashboards using R and R Shiny with step-by-step instructions - Learn how to combine open-source Big Data, machine learning and BI Tools to create low-cost business analytics applications - Understand corporate strategies for successful Big Data and data science projects - Go beyond general-purpose analytics to develop cutting-edge Big Data applications using emerging technologies Who this book is for The book is intended for existing and aspiring Big Data professionals who wish to become the go-to person in their organization when it comes to Big Data architecture, analytics, and governance. While no prior knowledge of Big Data or related technologies is assumed, it will be helpful to have some programming experience.

BIG DATA ANALYTICS

BIG DATA ANALYTICS
Author: Parag Kulkarni,Sarang Joshi,,Meta S. Brown
Publsiher: PHI Learning Pvt. Ltd.
Total Pages: 208
Release: 2016-07-07
ISBN: 8120351169
Category: Language Arts & Disciplines
Language: EN, FR, DE, ES & NL

BIG DATA ANALYTICS Book Excerpt:

The book is an unstructured data mining quest, which takes the reader through different features of unstructured data mining while unfolding the practical facets of Big Data. It emphasizes more on machine learning and mining methods required for processing and decision-making. The text begins with the introduction to the subject and explores the concept of data mining methods and models along with the applications. It then goes into detail on other aspects of Big Data analytics, such as clustering, incremental learning, multi-label association and knowledge representation. The readers are also made familiar with business analytics to create value. The book finally ends with a discussion on the areas where research can be explored.