Data Mining and Big Data

Data Mining and Big Data
Author: Ying Tan,Yuhui Shi
Publsiher: Springer
Total Pages: 340
Release: 2019-07-25
Genre: Computers
ISBN: 9789813295636

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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 full 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.

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: 2014-05-07
Genre: Computers
ISBN: 9781118920701

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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.

Big Data Mining for Climate Change

Big Data Mining for Climate Change
Author: Zhihua Zhang
Publsiher: Unknown
Total Pages: 344
Release: 2019-12
Genre: Electronic Book
ISBN: 9780128187036

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Big Data Mining for Climate Change addresses how to manage the vast amount of information available for analysis. Climate change and its environmental, economic and social consequences are widely recognized as the biggest, most interconnected problem facing humanity. There is a huge amount of potential information currently available...and it is growing exponentially. This book walks through the latest research and how to navigate the resources available using big data applications. It is appropriate for scientists and advanced students studying climate change from a number of disciplines, including the atmospheric sciences, oceanic sciences, geography, environment sciences, ecology, energy, economics, 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

Big Data Mining and Analytics

Big Data  Mining  and Analytics
Author: Stephan Kudyba
Publsiher: CRC Press
Total Pages: 325
Release: 2014-03-12
Genre: Computers
ISBN: 9781466568716

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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

Big Data Analytics and Knowledge Discovery

Big Data Analytics and Knowledge Discovery
Author: Sanjay Madria,Takahiro Hara
Publsiher: Springer
Total Pages: 418
Release: 2015-08-09
Genre: Computers
ISBN: 9783319227290

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This book constitutes the refereed proceedings of the 17th International Conference on Data Warehousing and Knowledge Discovery, DaWaK 2015, held in Valencia, Spain, September 2015. The 31 revised full papers presented were carefully reviewed and selected from 90 submissions. The papers are organized in topical sections similarity measure and clustering; data mining; social computing; heterogeneos networks and data; data warehouses; stream processing; applications of big data analysis; and big data.

Mining of Massive Datasets

Mining of Massive Datasets
Author: Jure Leskovec,Anand Rajaraman,Jeffrey David Ullman
Publsiher: Cambridge University Press
Total Pages: 476
Release: 2014-11-13
Genre: Computers
ISBN: 9781107077232

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Now in its second edition, this book focuses on practical algorithms for mining data from even the largest datasets.

Transparent Data Mining for Big and Small Data

Transparent Data Mining for Big and Small Data
Author: Tania Cerquitelli,Daniele Quercia,Frank Pasquale
Publsiher: Springer
Total Pages: 215
Release: 2017-05-09
Genre: Computers
ISBN: 9783319540245

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This book focuses on new and emerging data mining solutions that offer a greater level of transparency than existing solutions. Transparent data mining solutions with desirable properties (e.g. effective, fully automatic, scalable) are covered in the book. Experimental findings of transparent solutions are tailored to different domain experts, and experimental metrics for evaluating algorithmic transparency are presented. The book also discusses societal effects of black box vs. transparent approaches to data mining, as well as real-world use cases for these approaches.As algorithms increasingly support different aspects of modern life, a greater level of transparency is sorely needed, not least because discrimination and biases have to be avoided. With contributions from domain experts, this book provides an overview of an emerging area of data mining that has profound societal consequences, and provides the technical background to for readers to contribute to the field or to put existing approaches to practical use.

Social Big Data Mining

Social Big Data Mining
Author: Hiroshi Ishikawa
Publsiher: CRC Press
Total Pages: 268
Release: 2015-03-25
Genre: Computers
ISBN: 9781498710947

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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.

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
Genre: Social Science
ISBN: 9781529710991

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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.

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
Genre: Business & Economics
ISBN: 9781137379283

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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.

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: Brij Gupta,Ahmed A. Abd El-Latif,Dragan Perakovic
Publsiher: Unknown
Total Pages: 336
Release: 2021
Genre: Big data
ISBN: 1799884147

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"This book explores the key concepts of data mining and utilizing them on online social media platforms, offering valuable insight into data mining approaches for big data and sentiment analysis in online social media and covering many important security and other aspects and current trends"--

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
Genre: Computers
ISBN: 9781118618042

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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.

Big Data For Dummies

Big Data For Dummies
Author: Judith S. Hurwitz,Alan Nugent,Fern Halper,Marcia Kaufman
Publsiher: John Wiley & Sons
Total Pages: 336
Release: 2013-04-02
Genre: Computers
ISBN: 9781118644171

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Find the right big data solution for your business ororganization Big data management is one of the major challenges facingbusiness, industry, and not-for-profit organizations. Data setssuch as customer transactions for a mega-retailer, weather patternsmonitored by meteorologists, or social network activity can quicklyoutpace the capacity of traditional data management tools. If youneed to develop or manage big data solutions, you'll appreciate howthese four experts define, explain, and guide you through this newand often confusing concept. You'll learn what it is, why itmatters, and how to choose and implement solutions that work. Effectively managing big data is an issue of growing importanceto businesses, not-for-profit organizations, government, and ITprofessionals Authors are experts in information management, big data, and avariety of solutions Explains big data in detail and discusses how to select andimplement a solution, security concerns to consider, data storageand presentation issues, analytics, and much more Provides essential information in a no-nonsense,easy-to-understand style that is empowering Big Data For Dummies cuts through the confusion and helpsyou take charge of big data solutions for your organization.

Statistical and Machine Learning Data Mining

Statistical and Machine Learning Data Mining
Author: Bruce Ratner
Publsiher: CRC Press
Total Pages: 662
Release: 2017-07-12
Genre: Computers
ISBN: 9781351652384

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Interest in predictive analytics of big data has grown exponentially in the four years since the publication of Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data, Second Edition. In the third edition of this bestseller, the author has completely revised, reorganized, and repositioned the original chapters and produced 13 new chapters of creative and useful machine-learning data mining techniques. In sum, the 43 chapters of simple yet insightful quantitative techniques make this book unique in the field of data mining literature. What is new in the Third Edition: The current chapters have been completely rewritten. The core content has been extended with strategies and methods for problems drawn from the top predictive analytics conference and statistical modeling workshops. Adds thirteen new chapters including coverage of data science and its rise, market share estimation, share of wallet modeling without survey data, latent market segmentation, statistical regression modeling that deals with incomplete data, decile analysis assessment in terms of the predictive power of the data, and a user-friendly version of text mining, not requiring an advanced background in natural language processing (NLP). Includes SAS subroutines which can be easily converted to other languages. As in the previous edition, this book offers detailed background, discussion, and illustration of specific methods for solving the most commonly experienced problems in predictive modeling and analysis of big data. The author addresses each methodology and assigns its application to a specific type of problem. To better ground readers, the book provides an in-depth discussion of the basic methodologies of predictive modeling and analysis. While this type of overview has been attempted before, this approach offers a truly nitty-gritty, step-by-step method that both tyros and experts in the field can enjoy playing with.

Innovations in Big Data Mining and Embedded Knowledge

Innovations in Big Data Mining and Embedded Knowledge
Author: Anna Esposito,Antonietta M. Esposito,Lakhmi C. Jain
Publsiher: Springer
Total Pages: 276
Release: 2020-08-14
Genre: Technology & Engineering
ISBN: 3030159418

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This book addresses the usefulness of knowledge discovery through data mining. With this aim, contributors from different fields propose concrete problems and applications showing how data mining and discovering embedded knowledge from raw data can be beneficial to social organizations, domestic spheres, and ICT markets. Data mining or knowledge discovery in databases (KDD) has received increasing interest due to its focus on transforming large amounts of data into novel, valid, useful, and structured knowledge by detecting concealed patterns and relationships. The concept of knowledge is broad and speculative and has promoted epistemological debates in western philosophies. The intensified interest in knowledge management and data mining stems from the difficulty in identifying computational models able to approximate human behaviors and abilities in resolving organizational, social, and physical problems. Current ICT interfaces are not yet adequately advanced to support and simulate the abilities of physicians, teachers, assistants or housekeepers in domestic spheres. And unlike in industrial contexts where abilities are routinely applied, the domestic world is continuously changing and unpredictable. There are challenging questions in this field: Can knowledge locked in conventions, rules of conduct, common sense, ethics, emotions, laws, cultures, and experiences be mined from data? Is it acceptable for automatic systems displaying emotional behaviors to govern complex interactions based solely on the mining of large volumes of data? Discussing multidisciplinary themes, the book proposes computational models able to approximate, to a certain degree, human behaviors and abilities in resolving organizational, social, and physical problems. The innovations presented are of primary importance for: a. The academic research community b. The ICT market c. Ph.D. students and early stage researchers d. Schools, hospitals, rehabilitation and assisted-living centers e. Representatives from multimedia industries and standardization bodies