Data Mining And Knowledge Discovery For Big Data
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Data Mining And Knowledge Discovery For Big Data
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Author : Wesley W. Chu
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-09-24
Data Mining And Knowledge Discovery For Big Data written by Wesley W. Chu and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-09-24 with Technology & Engineering categories.
The field of data mining has made significant and far-reaching advances over the past three decades. Because of its potential power for solving complex problems, data mining has been successfully applied to diverse areas such as business, engineering, social media, and biological science. Many of these applications search for patterns in complex structural information. In biomedicine for example, modeling complex biological systems requires linking knowledge across many levels of science, from genes to disease. Further, the data characteristics of the problems have also grown from static to dynamic and spatiotemporal, complete to incomplete, and centralized to distributed, and grow in their scope and size (this is known as big data). The effective integration of big data for decision-making also requires privacy preservation. The contributions to this monograph summarize the advances of data mining in the respective fields. This volume consists of nine chapters that address subjects ranging from mining data from opinion, spatiotemporal databases, discriminative subgraph patterns, path knowledge discovery, social media, and privacy issues to the subject of computation reduction via binary matrix factorization.
Machine Learning For Data Science Handbook
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Author : Lior Rokach
language : en
Publisher: Springer Nature
Release Date : 2023-08-17
Machine Learning For Data Science Handbook written by Lior Rokach and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-08-17 with Mathematics categories.
This book is a major update to the very successful first and second editions (2005 and 2010) of Data Mining and Knowledge Discovery Handbook. Since the last edition, this field has continued to evolve and to gain popularity. Existing methods are constantly being improved and new methods, applications and aspects are introduced. The new title of this handbook and its content reflect these changes thoroughly. Some existing chapters have been brought up to date. In addition to major revision of the existing chapters, the new edition includes totally new topics, such as: deep learning, explainable AI, human factors and social issues and advanced methods for big-data. The significant enhancement to the content reflects the growth in importance of data science. The third edition is also a timely opportunity to incorporate many other changes based on peers and students’ feedback. This comprehensive handbook also presents a coherent and unified repository of data science major concepts, theories, methods, trends, challenges and applications. It covers all the crucial important machine learning methods used in data science. Today's accessibility and abundance of data make data science matters of considerable importance and necessity. Given the field's recent growth, it's not surprising that researchers and practitioners now have a wide range of methods and tools at their disposal. While statistics is fundamental for data science, methods originated from artificial intelligence, particularly machine learning, are also playing a significant role. This handbook aims to serve as the main reference for researchers in the fields of information technology, e-Commerce, information retrieval, data science, machine learning, data mining, databases and statistics as well as advanced level students studying computer science or electrical engineering. Practitioners working within these related fields and data scientists will also want to purchase this handbook as a reference.
Big Data Analytics And Knowledge Discovery
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Author : Carlos Ordonez
language : en
Publisher: Springer
Release Date : 2019-08-19
Big Data Analytics And Knowledge Discovery written by Carlos Ordonez and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-08-19 with Computers categories.
This book constitutes the refereed proceedings of the 21st International Conference on Big Data Analytics and Knowledge Discovery, DaWaK 2019, held in Linz, Austria, in September 2019. The 12 full papers and 10 short papers presented were carefully reviewed and selected from 61 submissions. The papers are organized in the following topical sections: Applications; patterns; RDF and streams; big data systems; graphs and machine learning; databases.
Data Mining
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Author : Krzysztof J. Cios
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-10-05
Data Mining written by Krzysztof J. Cios and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-10-05 with Computers categories.
“If you torture the data long enough, Nature will confess,” said 1991 Nobel-winning economist Ronald Coase. The statement is still true. However, achieving this lofty goal is not easy. First, “long enough” may, in practice, be “too long” in many applications and thus unacceptable. Second, to get “confession” from large data sets one needs to use state-of-the-art “torturing” tools. Third, Nature is very stubborn — not yielding easily or unwilling to reveal its secrets at all. Fortunately, while being aware of the above facts, the reader (a data miner) will find several efficient data mining tools described in this excellent book. The book discusses various issues connecting the whole spectrum of approaches, methods, techniques and algorithms falling under the umbrella of data mining. It starts with data understanding and preprocessing, then goes through a set of methods for supervised and unsupervised learning, and concludes with model assessment, data security and privacy issues. It is this specific approach of using the knowledge discovery process that makes this book a rare one indeed, and thus an indispensable addition to many other books on data mining. To be more precise, this is a book on knowledge discovery from data. As for the data sets, the easy-to-make statement is that there is no part of modern human activity left untouched by both the need and the desire to collect data. The consequence of such a state of affairs is obvious.
Big Data Analytics And Knowledge Discovery
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Author : Carson K. Leung
language : en
Publisher: Springer Nature
Release Date : 2025-09-20
Big Data Analytics And Knowledge Discovery written by Carson K. Leung and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-09-20 with Computers categories.
This book constitutes the proceedings of the 27th International Conference on Big Data Analytics and Knowledge Discovery, DaWaK 2025, held in Bangkok, Thailand, during August 25–27, 2025. The 12 full and 14 short papers included in the proceedings were carefully reviewed and selected from 62 submissions. The proceedings also contain one keynote talk, one invited talk and one tutorial. The papers were organized in topical sections as follows: Data mining and knowledge discovery; sequential data analytics and recommendation systems; graph data processing and analytics; data management and indices; Large language Models (LLMs); and Neural Networks.
Data Mining
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Author : Jiawei Han
language : en
Publisher: Morgan Kaufmann
Release Date : 2022-07-02
Data Mining written by Jiawei Han and has been published by Morgan Kaufmann this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-07-02 with Computers categories.
Data 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. 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 classificcation 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. - 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 propagattion), data mining methodologie 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 - Visit the author-hosted companion site, https://hanj.cs.illinois.edu/bk4/ for downloadable lecture slides and errata
Data Mining Southeast Asia Edition
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Author : Jiawei Han
language : en
Publisher: Elsevier
Release Date : 2006-04-06
Data Mining Southeast Asia Edition written by Jiawei Han and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-04-06 with Computers categories.
Our ability to generate and collect data has been increasing rapidly. Not only are all of our business, scientific, and government transactions now computerized, but the widespread use of digital cameras, publication tools, and bar codes also generate data. On the collection side, scanned text and image platforms, satellite remote sensing systems, and the World Wide Web have flooded us with a tremendous amount of data. This explosive growth has generated an even more urgent need for new techniques and automated tools that can help us transform this data into useful information and knowledge. Like the first edition, voted the most popular data mining book by KD Nuggets readers, this book explores concepts and techniques for the discovery of patterns hidden in large data sets, focusing on issues relating to their feasibility, usefulness, effectiveness, and scalability. However, since the publication of the first edition, great progress has been made in the development of new data mining methods, systems, and applications. This new edition substantially enhances the first edition, and new chapters have been added to address recent developments on mining complex types of data— including stream data, sequence data, graph structured data, social network data, and multi-relational data. - A comprehensive, practical look at the concepts and techniques you need to know to get the most out of real business data - Updates that incorporate input from readers, changes in the field, and more material on statistics and machine learning - Dozens of algorithms and implementation examples, all in easily understood pseudo-code and suitable for use in real-world, large-scale data mining projects - Complete classroom support for instructors at www.mkp.com/datamining2e companion site
Big Data Analytics And Knowledge Discovery
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Author : Carlos Ordonez
language : en
Publisher: Springer
Release Date : 2018-08-20
Big Data Analytics And Knowledge Discovery written by Carlos Ordonez and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-08-20 with Computers categories.
This book constitutes the refereed proceedings of the 20th International Conference on Big Data Analytics and Knowledge Discovery, DaWaK 2018, held in Regensburg, Germany, in September 2018. The 13 revised full papers and 17 short papers presented were carefully reviewed and selected from 76 submissions. The papers are organized in the following topical sections: Graph analytics; case studies; classification and clustering; pre-processing; sequences; cloud and database systems; and data mining.
Advances In Knowledge Discovery And Data Mining
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Author : De-Nian Yang
language : en
Publisher: Springer Nature
Release Date : 2024-04-24
Advances In Knowledge Discovery And Data Mining written by De-Nian Yang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-04-24 with Computers categories.
The 6-volume set LNAI 14645-14650 constitutes the proceedings of the 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2024, which took place in Taipei, Taiwan, during May 7–10, 2024. The 177 papers presented in these proceedings were carefully reviewed and selected from 720 submissions. They deal with new ideas, original research results, and practical development experiences from all KDD related areas, including data mining, data warehousing, machine learning, artificial intelligence, databases, statistics, knowledge engineering, big data technologies, and foundations.
Intelligent Knowledge
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Author : Yong Shi
language : en
Publisher: Springer
Release Date : 2015-05-08
Intelligent Knowledge written by Yong Shi and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-05-08 with Business & Economics categories.
This book is mainly about an innovative and fundamental method called “intelligent knowledge” to bridge the gap between data mining and knowledge management, two important fields recognized by the information technology (IT) community and business analytics (BA) community respectively. The book includes definitions of the “first-order” analytic process, “second-order” analytic process and intelligent knowledge, which have not formally been addressed by either data mining or knowledge management. Based on these concepts, which are especially important in connection with the current Big Data movement, the book describes a framework of domain-driven intelligent knowledge discovery. To illustrate its technical advantages for large-scale data, the book employs established approaches, such as Multiple Criteria Programming, Support Vector Machine and Decision Tree to identify intelligent knowledge incorporated with human knowledge. The book further shows its applicability by means of real-life data analyses in the contexts of internet business and traditional Chinese medicines.