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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, 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: 17th Australasian Conference, AusDM 2019, Adelaide, SA, Australia, December 2–5, 2019, Proceedings (Communications in Computer and Information Science #1127)
by Thuc D. Le Kok-Leong Ong Yanchang Zhao Warren H. Jin Sebastien Wong Lin Liu Graham WilliamsThis book constitutes the refereed proceedings of the 17th Australasian Conference on Data Mining, AusDM 2019, held in Adelaide, SA, Australia, in December 2019.The 20 revised full papers presented were carefully reviewed and selected from 56 submissions. The papers are organized in sections on research track, application track, and industry showcase.
Data Mining: 20th Australasian Conference, AusDM 2022, Western Sydney, Australia, December 12–15, 2022, Proceedings (Communications in Computer and Information Science #1741)
by Laurence A. F. Park Heitor Murilo Gomes Maryam Doborjeh Yee Ling Boo Yun Sing Koh Yanchang Zhao Graham Williams Simeon SimoffThis book constitutes the refereed proceedings of the 20th Australasian Conference on Data Mining, AusDM 2022, held in Western Sydney, Australia, during December 12–15, 2022. The 17 full papers included in this book were carefully reviewed and selected from 44 submissions. They were organized in topical sections as research track and application track.
Data Mining: A Tutorial-Based Primer, Second Edition (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)
by Richard J. Roiger"Dr. Roiger does an excellent job of describing in step by step detail formulae involved in various data mining algorithms, along with illustrations. In addition, his tutorials in Weka software provide excellent grounding for students in comprehending the underpinnings of Machine Learning as applied to Data Mining. The inclusion of?RapidMiner software tutorials and examples in the book is also a definite plus since it is one of the most popular Data Mining software platforms in use today." --Robert Hughes, Golden Gate University, San Francisco, CA, USA Data Mining: A Tutorial-Based Primer, Second Edition provides a comprehensive introduction to data mining with a focus on model building and testing, as well as on interpreting and validating results. The text guides students to understand how data mining can be employed to solve real problems and recognize whether a data mining solution is a feasible alternative for a specific problem. Fundamental data mining strategies, techniques, and evaluation methods are presented and implemented with the help of two well-known software tools. Several new topics have been added to the second edition including an introduction to Big Data and data analytics, ROC curves, Pareto lift charts, methods for handling large-sized, streaming and imbalanced data, support vector machines, and extended coverage of textual data mining. The second edition contains tutorials for attribute selection, dealing with imbalanced data, outlier analysis, time series analysis, mining textual data, and more. The text provides in-depth coverage of RapidMiner Studio and Weka’s Explorer interface. Both software tools are used for stepping students through the tutorials depicting the knowledge discovery process. This allows the reader maximum flexibility for their hands-on data mining experience.
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: 16th Australasian Conference, Ausdm 2018, Bahrurst, Nsw, Australia, November 28-30, 2018, Revised Selected Papers (Communications in Computer and Information Science #996)
by David Stirling Rafiqul Islam Yun Sing Koh Yanchang Zhao Graco Warwick Chang-Tsun Li Zahidul IslamThis book constitutes the refereed proceedings of the 16th Australasian Conference on Data Mining, AusDM 2018, held in Bathurst, NSW, Australia, in November 2018. <P><P> The 27 revised full papers presented together with 3 short papers were carefully reviewed and selected from 80 submissions. The papers are organized in topical sections on classification task; transport, environment, and energy; applied data mining; privacy and clustering; statistics in data science; health, software and smartphone; image data mining; industry showcase.
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: 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: Theory, Methodology, Techniques, And Applications (Lecture Notes in Computer Science #3755)
by Graham Williams Kok-Leong Ong Lin Liu Lianhua Chi David Stirling Yee Ling BooThis 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 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 Algorithms in C++: Data Patterns and Algorithms for Modern Applications
by Timothy MastersIn my decades of custom programming and consultation, I have explored diverse applications, including automated analysis of high-altitude photographs, automated medical diagnosis, realtime detection of threatening military vehicles, and automated trading of financial markets. A common thread in all of these applications is that I was faced with a multitude of observed or computed variables, and my task involved finding and analyzing relationships among these variables. As a result, I have accumulated a wealth of algorithms for doing so. This book presents theoretical and intuitive justifications, along with highly commented source code, for my favorite data-mining techniques. This book makes no pretense of being 'complete' in any manner whatsoever. Please do not be annoyed if your own favorite techniques did not make my cut, or if the book ignores some popular standard techniques. These are simply the algorithms that I have found most useful in my own work over the years. Some of them are venerable old techniques such as the use of maximum-likelihood factor analysis for determining the degree to which variables contain unique information, versus being redundant due to hidden common factors impacting several variables. Some of them are powerful modern techniques, such as Combinatorially Symmetric Cross Validation for determining if a model is hampered by overfitting, or Feature Weighting as Regularized Energy-Based Learning for ranking variables in predictive power when there are too few training cases to employ traditional methods. Some of them are (I believe) my own invention, such as a method for clustering variables in the restricted context of a subspace of interest, and visual display of anomalous regions in which joint and marginal densities conflict, or in which contribution to mutual information is concentrated. But all of them share a great quality: I have found them to be exceptionally useful in my own data-mining endeavors. I suspect that you will as well.
Data Mining and Analysis
by Mohammed J. Zaki Wagner Meira Jr.The fundamental algorithms in data mining and analysis form the basis for the emerging field of data science, which includes automated methods to analyze patterns and models for all kinds of data, with applications ranging from scientific discovery to business intelligence and analytics. This textbook for senior undergraduate and graduate data mining courses provides a broad yet in-depth overview of data mining, integrating related concepts from machine learning and statistics. The main parts of the book include exploratory data analysis, pattern mining, clustering, and classification. The book lays the basic foundations of these tasks, and also covers cutting-edge topics such as kernel methods, high-dimensional data analysis, and complex graphs and networks. With its comprehensive coverage, algorithmic perspective, and wealth of examples, this book offers solid guidance in data mining for students, researchers, and practitioners alike.
Data Mining and Analytics in Healthcare Management: Applications and Tools (International Series in Operations Research & Management Science #341)
by David L. Olson Özgür M. ArazThis book presents data mining methods in the field of healthcare management in a practical way. Healthcare quality and disease prevention are essential in today’s world. Healthcare management faces a number of challenges, e.g. reducing patient growth through disease prevention, stopping or slowing disease progression, and reducing healthcare costs while improving quality of care. The book provides an overview of current healthcare management problems and highlights how analytics and knowledge management have been used to better cope with them. It then demonstrates how to use descriptive and predictive analytics tools to help address these challenges. In closing, it presents applications of software solutions in the context of healthcare management. Given its scope, the book will appeal to a broad readership, from researchers and students in the operations research and management field to practitioners such as data analysts and decision-makers who work in the healthcare sector.
Data Mining and Big Data: 4th International Conference, DMBD 2019, Chiang Mai, Thailand, July 26–30, 2019, Proceedings (Communications in Computer and Information Science #1071)
by Ying Tan Yuhui ShiThis 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.
Data Mining and Big Data: 7th International Conference, DMBD 2022, Beijing, China, November 21–24, 2022, Proceedings, Part II (Communications in Computer and Information Science #1745)
by Ying Tan Yuhui ShiThis two-volume set, CCIS 1744 and CCIS 1745 book constitutes the 7th International Conference, on Data Mining and Big Data, DMBD 2022, held in Beijing, China, in November 21–24, 2022.The 62 full papers presented in this two-volume set included in this book were carefully reviewed and selected from 135 submissions. The papers present the latest research on advantages in theories, technologies, and applications in data mining and big data. The volume covers many aspects of data mining and big data as well as intelligent computing methods applied to all fields of computer science, machine learning, data mining and knowledge discovery, data science, etc.
Data Mining and Big Data: 7th International Conference, DMBD 2022, Beijing, China, November 21–24, 2022, Proceedings, Part I (Communications in Computer and Information Science #1744)
by Ying Tan Yuhui ShiThis two-volume set, CCIS 1744 and CCIS 1745 book constitutes the 7th International Conference, on Data Mining and Big Data, DMBD 2022, held in Beijing, China, in November 21–24, 2022.The 62 full papers presented in this two-volume set included in this book were carefully reviewed and selected from 135 submissions. The papers present the latest research on advantages in theories, technologies, and applications in data mining and big data. The volume covers many aspects of data mining and big data as well as intelligent computing methods applied to all fields of computer science, machine learning, data mining and knowledge discovery, data science, etc.
Data Mining and Big Data: 8th International Conference, DMBD 2023, Sanya, China, December 9–12, 2023, Proceedings, Part I (Communications in Computer and Information Science #2017)
by Ying Tan Yuhui ShiThis two-volume set, CCIS 2017 and 2018 constitutes the 8th International Conference, on Data Mining and Big Data, DMBD 2023, held in Sanya, China, in December 2023. The 38 full papers presented in this two-volume set included in this book were carefully reviewed and selected from 79 submissions. The papers present the latest research on advantages in theories, technologies, and applications in data mining and big data. The volume covers many aspects of data mining and big data as well as intelligent computing methods applied to all fields of computer science, machine learning, data mining and knowledge discovery, data science, etc.
Data Mining and Big Data: 8th International Conference, DMBD 2023, Sanya, China, December 9–12, 2023, Proceedings, Part II (Communications in Computer and Information Science #2018)
by Ying Tan Yuhui ShiThis two-volume set, CCIS 2017 and 2018 constitutes the 8th International Conference, on Data Mining and Big Data, DMBD 2023, held in Sanya, China, in December 2023. The 38 full papers presented in this two-volume set included in this book were carefully reviewed and selected from 79 submissions. The papers present the latest research on advantages in theories, technologies, and applications in data mining and big data. The volume covers many aspects of data mining and big data as well as intelligent computing methods applied to all fields of computer science, machine learning, data mining and knowledge discovery, data science, etc.
Data Mining and Big Data: Third International Conference, DMBD 2018, Shanghai, China, June 17–22, 2018, Proceedings (Lecture Notes in Computer Science #10943)
by Ying Tan Yuhui Shi Qirong TangThis 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
Data Mining and Big Data: 5th International Conference, DMBD 2020, Belgrade, Serbia, July 14–20, 2020, Proceedings (Communications in Computer and Information Science #1234)
by Ying Tan Yuhui Shi Milan TubaThis book constitutes refereed proceedings of the 5th International Conference on Data Mining and Big Data, DMBD 2020, held in July 2020. Due to the COVID-19 pandemic the conference was held in a fully virtual format. The 7 full papers and 3 short papers presented in this volume were carefully reviewed and selected from 39 submissions. The papers present the latest research on advantages in theories, technologies, and applications in data mining and big data. The volume covers many aspects of data mining and big data as well as intelligent computing methods applied to all fields of computer science, machine learning, data mining and knowledge discovery, data science, etc.
Data Mining and Big Data: 6th International Conference, DMBD 2021, Guangzhou, China, October 20–22, 2021, Proceedings, Part II (Communications in Computer and Information Science #1454)
by Ying Tan Yuhui Shi Albert Zomaya Hongyang Yan Jun CaiThis two-volume set, CCIS 1453 and CCIS 1454, constitutes refereed proceedings of the 6th International Conference on Data Mining and Big Data, DMBD 2021, held in Guangzhou, China, in October 2021. The 57 full papers and 28 short papers presented in this two-volume set were carefully reviewed and selected from 258 submissions. The papers present the latest research on advantages in theories, technologies, and applications in data mining and big data. The volume covers many aspects of data mining and big data as well as intelligent computing methods applied to all fields of computer science, machine learning, data mining and knowledge discovery, data science, etc.
Data Mining and Big Data: 6th International Conference, DMBD 2021, Guangzhou, China, October 20–22, 2021, Proceedings, Part I (Communications in Computer and Information Science #1453)
by Ying Tan Yuhui Shi Albert Zomaya Hongyang Yan Jun CaiThis two-volume set, CCIS 1453 and CCIS 1454, constitutes refereed proceedings of the 6th International Conference on Data Mining and Big Data, DMBD 2021, held in Guangzhou, China, in October 2021. The 57 full papers and 28 short papers presented in this two-volume set were carefully reviewed and selected from 258 submissions. The papers present the latest research on advantages in theories, technologies, and applications in data mining and big data. The volume covers many aspects of data mining and big data as well as intelligent computing methods applied to all fields of computer science, machine learning, data mining and knowledge discovery, data science, etc.
Data Mining and Big Data: Second International Conference, DMBD 2017, Fukuoka, Japan, July 27 – August 1, 2017, Proceedings (Lecture Notes in Computer Science #10387)
by Ying Tan, Hideyuki Takagi and Yuhui ShiThis book constitutes the refereed proceedings of the Second International Conference on Data Mining and Big Data, DMBD 2017, held in Fukuoka, Japan, in July/August 2017. The 53 papers presented in this volume were carefully reviewed and selected from 96 submissions. They were organized in topical sections named: association analysis; clustering; prediction; classification; schedule and sequence analysis; big data; data analysis; data mining; text mining; deep learning; high performance computing; knowledge base and its framework; and fuzzy control.